Abstract— At Early exposure of patients with dignified risk of developing diabetes mellitus is so hyper critical to the bettered prevention and global clinical management of these patients. In an existing system, apriori algorithm is used to find the itemsets for association rules but it is not efficient in finding itemsets and it uses only four association rules for finding the risk of diabetes mellitus so it have low precision. In this paper we are focusing to implement association rule mining to electronic medical records to detect set of danger factors and their equivalent or identical subpopulations that indicates patients at especially steep risk of progressing diabetes. Association rule mining accomplishes a very bulky set of rules for summarizing the EMR with huge dimensionability. We proposed a system in enlargement to combine risk of diabetes for the purpose of finding an suitable summary for this we use ten association rule and using the reorder algorithm for finding the itemsets and rules. For identifying the risk we considered four association rule set summarization techniques and organised a related calculation to support counselling with respect to their applicability merits and demerits and provide solutions to reduce the risk of diabetes. The above four methods having its fair strength but the bus algorithm developed the best acceptable summary.
Abstract: Now a days detection of patients with elevated risk of diabetes mellitus is developing critical to the improved prevention and overall health management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to invent sets of risk factors and their corresponding subpopulations that represent patients which have high risk of developing diabetes. With the high linearity of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy medical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, advantages and drawbacks. We proposed extensions to incorporate risk of diabetes into the process of finding an optimum summary. We evaluated these modified techniques on a real-world border line diabetes patient associate. We found that all four methods gives summaries that described subpopulations at high risk of diabetes with every method having its clear strength. In this extension to the Bottom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
Keywords: Agile model, Association rules, Association rule summarization, Data mining, Survival analysis, Fuzzy Clustering.
Title: Diabetes Mellitus Prediction System Using Data Mining
Author: Yamini Amrale, Arti Shedge, Sonal Singh, Anjum Shaikh
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
This summarizes a study that combines association rule mining and propensity score matching to identify subgroups of pre-diabetic patients where statin use has different effects on progression to diabetes. Association rule mining is used to discover phenotypes defined by risk factors. Within each phenotype, propensity score matching is applied to identify comparable statin-treated and untreated patient pairs. This allows estimating the relative risk of diabetes for statin users compared to non-users within each phenotype, accounting for confounding factors. The method was applied to a dataset of pre-diabetic patients to identify subgroups where statins had different effects on developing overt diabetes over 5 years.
Ascendable Clarification for Coronary Illness Prediction using Classification...ijtsrd
This document describes a study that used data mining techniques to predict coronary heart disease. The study used the WEKA data mining tool to implement a Naive Bayes classifier on a dataset of 4,645 records with 11 attributes related to heart disease. Feature selection and bagging were then combined with Naive Bayes to improve classification accuracy. Experimental results showed that the combined approach of Naive Bayes, attribute selection, and bagging (NBASB) achieved a classification accuracy of 97%, outperforming Naive Bayes alone or with bagging only. This approach enhances predictive performance while reducing computational time for coronary heart disease prediction.
Allometry scaling is used to predict pharmacokinetic parameters such as volume of distribution, clearance, and half-life in humans based on animal data. It involves plotting parameters against body weight on a log-log scale to determine relationships. Two approaches for interspecies scaling are physiological models using organ sizes and rates, and empirical allometric methods. Accurate prediction requires data from multiple animal species, though some use fewer. The goal of allometric scaling is to safely estimate first human doses during drug development.
This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
IRJET- Extending Association Rule Summarization Techniques to Assess Risk of ...IRJET Journal
The document discusses using association rule mining and k-means clustering to identify risk factors for diabetes from electronic medical records. It reviews existing techniques for summarizing large sets of association rules generated from medical data and proposes using k-means clustering as an improved method. The k-means algorithm clusters patient data into groups based on similarity and identifies representative risk factor patterns within each cluster, providing a concise summary for clinicians to assess diabetes risk.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Abstract: Now a days detection of patients with elevated risk of diabetes mellitus is developing critical to the improved prevention and overall health management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to invent sets of risk factors and their corresponding subpopulations that represent patients which have high risk of developing diabetes. With the high linearity of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy medical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, advantages and drawbacks. We proposed extensions to incorporate risk of diabetes into the process of finding an optimum summary. We evaluated these modified techniques on a real-world border line diabetes patient associate. We found that all four methods gives summaries that described subpopulations at high risk of diabetes with every method having its clear strength. In this extension to the Bottom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
Keywords: Agile model, Association rules, Association rule summarization, Data mining, Survival analysis, Fuzzy Clustering.
Title: Diabetes Mellitus Prediction System Using Data Mining
Author: Yamini Amrale, Arti Shedge, Sonal Singh, Anjum Shaikh
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
This summarizes a study that combines association rule mining and propensity score matching to identify subgroups of pre-diabetic patients where statin use has different effects on progression to diabetes. Association rule mining is used to discover phenotypes defined by risk factors. Within each phenotype, propensity score matching is applied to identify comparable statin-treated and untreated patient pairs. This allows estimating the relative risk of diabetes for statin users compared to non-users within each phenotype, accounting for confounding factors. The method was applied to a dataset of pre-diabetic patients to identify subgroups where statins had different effects on developing overt diabetes over 5 years.
Ascendable Clarification for Coronary Illness Prediction using Classification...ijtsrd
This document describes a study that used data mining techniques to predict coronary heart disease. The study used the WEKA data mining tool to implement a Naive Bayes classifier on a dataset of 4,645 records with 11 attributes related to heart disease. Feature selection and bagging were then combined with Naive Bayes to improve classification accuracy. Experimental results showed that the combined approach of Naive Bayes, attribute selection, and bagging (NBASB) achieved a classification accuracy of 97%, outperforming Naive Bayes alone or with bagging only. This approach enhances predictive performance while reducing computational time for coronary heart disease prediction.
Allometry scaling is used to predict pharmacokinetic parameters such as volume of distribution, clearance, and half-life in humans based on animal data. It involves plotting parameters against body weight on a log-log scale to determine relationships. Two approaches for interspecies scaling are physiological models using organ sizes and rates, and empirical allometric methods. Accurate prediction requires data from multiple animal species, though some use fewer. The goal of allometric scaling is to safely estimate first human doses during drug development.
This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
IRJET- Extending Association Rule Summarization Techniques to Assess Risk of ...IRJET Journal
The document discusses using association rule mining and k-means clustering to identify risk factors for diabetes from electronic medical records. It reviews existing techniques for summarizing large sets of association rules generated from medical data and proposes using k-means clustering as an improved method. The k-means algorithm clusters patient data into groups based on similarity and identifies representative risk factor patterns within each cluster, providing a concise summary for clinicians to assess diabetes risk.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Statistical multivariate analysis to infer the presence breast cancerFahad B. Mostafa
The primary aim of this multivariate analysis is to show statistical significance of many statistical technique to analysis multivariate data. To do this we start with exploratory study to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis of 116 women. To conduct this process, we will plot the sample data and show the type of distribution it follows. Main aim of this research is to reduce dimensionality using eigen decomposition of data matrix. To perform it we use the most useful PCA method. Finally, we want to find some hypothesis tests for finding the normality assumption, equal mean and covariance test, as well as simultaneous confidence interval for our data sets. Moreover, to predict breast cancer we used logistic regression model as well as confusion matrix to show how confuse our model.
Cost-effectiveness of electroconvulsive therapy compared to repetitive transc...Pydesalud
Póster sobre el coste-efectividad de la terapia electroconvulsiva frente a la estimulación magnética transcraneal en depresión resistente. Fue presentado por Laura Vallejo (técnica del SESCS) en la XXXIV edición de las Jornadas de Economía de la Salud organizadas por la Asociación de Economía de la Salud (AES). Pamplona, 27-30 mayo de 2014.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
An Experimental Study of Diabetes Disease Prediction System Using Classificat...IOSRjournaljce
Data mining means to the process of collecting, searching through, and analyzing a large amount of data in a database. Classification in one of the well-known data mining techniques for analyzing the performance of Naive Bayes, Random Forest, and Naïve Bayes tree (NB-Tree) classifier during the classification to improve precision, recall, f-measure, and accuracy. These three algorithms, of Naive Bayes, Random Forest, and NB-Tree are useful and efficient, has been tested in the medical dataset for diabetes disease and solving classification problem in data mining. In this paper, we compare the three different algorithms, and results indicate the Naive Bayes algorithms are able to achieve high accuracy rate along with minimum error rate when compared to other algorithms.
The correlation between pretreatment serum lactate dehydrogenase (LDH) levels...chaichana14
Objective: This study aimed to examine the relationship between pretreatment serum LDH levels and factors in advanced solid tumor to find out information for clinical use.
Materials and Methods: This is a cross-sectional study. Data of pretreatment LDH levels in 35 patients with advanced solid tumor at Cancer Clinic, Division of Medical oncology , Department of Internal Medicine, Buddhasothorn Hospital, were collected. And each patient was followed up for 6 months.
Results: The results showed that the pretreatment serum LDH levels did not correlate with factors including age, ECOG performance status, body mass index (BMI), tumor burden, site of metastasis, resection of the primary tumor, received systemic treatment, and 6-month mortality. However, High LDH levels were correlated with liver metastasis and being untreated by systemic treatment with statistical significance.(2-tailed significance, p = 0.001)
Conclusion: Pretreatment serum LDH levels were not found to correlate with the above mentioned factors; nevertheless, High Pretreatment serum LDH level was found to correlate with liver metastasis and correlate with and being untreated by systemic treatment. Data yet had limitations. However, the benefits of this research can be further studied in the future to find a marker that can help to evaluate and follow-up cancer patients.
Keywords: Lactate Dehydrogenase(LDH), Advanced Solid Tumor, Correlation
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive...IJECEIAES
The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
Harnessing Data to Improve Health Equity - Dr. Ali MokdadLauren Johnson
1) The document discusses methods used by the Institute for Health Metrics and Evaluation (IHME) to conduct comprehensive analyses of global, national, and subnational disease burden through their Global Burden of Disease (GBD) study.
2) Key methods discussed include garbage code redistribution to reassign unspecified causes of death, Bayesian meta-regression to estimate incidence and prevalence, and small area statistical models that borrow strength across space, time, and covariates to produce estimates of disease burden for locations with limited data.
3) The GBD study aims to quantify health loss from major diseases, injuries, and risk factors globally and over time in order to help identify and address the world's most pressing health challenges.
Estimating the Survival Function of HIV AIDS Patients using Weibull Modelijtsrd
This work provides information on the survival times of a cohort of infected individuals. The mean survival time was obtained as 22.579 months from the resultant estimate of the shape parameter =1.156 and scale parameter =0.0256 from Weibull 7 simulation of n = 500. Confidence intervals were also obtained for the two parameters at = 0.05 and it was found that the estimates are highly reliable. R. A. Adeleke | O. D. Ogunwale "Estimating the Survival Function of HIV/AIDS Patients using Weibull Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30636.pdf Paper Url :https://www.ijtsrd.com/mathemetics/statistics/30636/estimating-the-survival-function-of-hivaids-patients-using-weibull-model/r-a-adeleke
ABSTRACT
Objective: Stroke is one of the leading causes of death and disabilities worldwide. Cost-effectiveness analysis helps identify neglected opportunities
by highlighting interventions that are relatively inexpensive, yet have the potential to reduce the disease burden substantially. In India, there are
wide social and economic disparities. Socioeconomic environment influences occupation, lifestyle, and nutrition of social classes which in turn would
influence the prevalence and profile of stroke. By reduction of delays in access to hospital and improving provision of affordable treatments can
reduce morbidity and mortality in patients with stroke in India. This study is designed to measure and compare the costs (resources consumed) and
consequences (clinical, economic, and humanistic) of pharmaceutical products and services and their impact on individuals, healthcare systems and
society.
Methods: The purpose of this study is to analyze and conduct a cost-effectiveness analysis for the treatment of stroke in Guntur City Hospitals.
The patients were treated either with aspirin or clopidogrel. The health outcomes were measured using Modified Rankin Scale, A prominent risk
assessment scale for stroke. The pharmacoeconomic data were computed from the patient data collection forms.
Result: The incremental cost-effectiveness ratio of aspirin and clopidogrel were calculated to be Rs. 8046.2/year.
Conclusion: The study concludes that aspirin has the increased socioeconomic impact when compared to Clopidogrel and we can see that the earlier
therapy has supported discharge, home-based rehabilitation along with reduced hospital stay and hence preferable.
Keywords: Stroke, Pharmacoeconomics, Cost-effectiveness analysis, Aspirin, Clopidogrel, Incremental cost-effectiveness ratio.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This study assessed the costs and effects of different degrees of task shifting for anti-retroviral therapy (ART) from physicians to other health professionals in Ethiopia. The study found that (1) facilities with maximal task shifting, where non-physicians performed most ART tasks, had similar patient outcomes and costs as facilities with minimal/moderate task shifting; (2) over 88% of patients remained active on ART after two years across all facility types; and (3) maximal task shifting cost $36 more per patient over two years but resulted in 0.4% fewer patients remaining active, though this difference was not statistically significant.
Machine learning and operations research to find diabetics at risk for readmisison.
A team of researchers was able to apply machine learning to reduce readmissions for diabetics, see "Identifying diabetic patients with high risk of readmission" (Bhuvan,Kumar, Zafar, Aand Kishore, 2016).
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
Improving the performance of k nearest neighbor algorithm for the classificat...IAEME Publication
The document discusses improving the performance of the k-nearest neighbor (kNN) algorithm for classifying diabetes datasets with missing values. It first provides background on diabetes and challenges with missing data. It then describes various data preprocessing techniques used to handle missing values, including mean imputation. The document outlines the kNN classification algorithm and metrics like accuracy and error rate to evaluate performance. It applies these techniques to the Pima Indian diabetes dataset and finds that imputing missing values along with suitable preprocessing like normalization increases classification accuracy compared to ignoring missing values or imputation alone.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Women who test positive for one of the two breast cancer susceptibility genes, BRCA1 and BRCA2, increase their risk by 45-55 percent. Currently, there are no specific physical activity recommendations for these women. However, research supports the positive effect of exercise on reducing breast cancer risk by reducing BMI, adipose tissue, and damage caused by lipid peroxidation.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
This document discusses technological tools for customer relationship management (CRM). It covers the main functionality of CRM applications including sales force automation, campaign management, and customer service and support. Specifically, it describes the functionality required for campaign management like workflow, segmentation, personalization, execution, response management, and response modeling. It also discusses the sales cycle and functionality needed for sales force automation, including interfacing with marketing campaigns and business contact/account management. Finally, it outlines the full customer service cycle from logging requests to billing.
Data mining is a process that analyzes data from different perspectives to discover patterns and relationships. It uses techniques like clustering, regression, and association rules. Clustering groups data into clusters, regression analyzes the statistical relationship between two variables, and association rules are used to analyze sales transactions and customer data. Data mining has various uses including sales/marketing, risk assessment, fraud detection, and customer care. It offers advantages such as being easy to implement, efficiently finding information, increased profitability, and high-quality results.
Statistical multivariate analysis to infer the presence breast cancerFahad B. Mostafa
The primary aim of this multivariate analysis is to show statistical significance of many statistical technique to analysis multivariate data. To do this we start with exploratory study to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis of 116 women. To conduct this process, we will plot the sample data and show the type of distribution it follows. Main aim of this research is to reduce dimensionality using eigen decomposition of data matrix. To perform it we use the most useful PCA method. Finally, we want to find some hypothesis tests for finding the normality assumption, equal mean and covariance test, as well as simultaneous confidence interval for our data sets. Moreover, to predict breast cancer we used logistic regression model as well as confusion matrix to show how confuse our model.
Cost-effectiveness of electroconvulsive therapy compared to repetitive transc...Pydesalud
Póster sobre el coste-efectividad de la terapia electroconvulsiva frente a la estimulación magnética transcraneal en depresión resistente. Fue presentado por Laura Vallejo (técnica del SESCS) en la XXXIV edición de las Jornadas de Economía de la Salud organizadas por la Asociación de Economía de la Salud (AES). Pamplona, 27-30 mayo de 2014.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
An Experimental Study of Diabetes Disease Prediction System Using Classificat...IOSRjournaljce
Data mining means to the process of collecting, searching through, and analyzing a large amount of data in a database. Classification in one of the well-known data mining techniques for analyzing the performance of Naive Bayes, Random Forest, and Naïve Bayes tree (NB-Tree) classifier during the classification to improve precision, recall, f-measure, and accuracy. These three algorithms, of Naive Bayes, Random Forest, and NB-Tree are useful and efficient, has been tested in the medical dataset for diabetes disease and solving classification problem in data mining. In this paper, we compare the three different algorithms, and results indicate the Naive Bayes algorithms are able to achieve high accuracy rate along with minimum error rate when compared to other algorithms.
The correlation between pretreatment serum lactate dehydrogenase (LDH) levels...chaichana14
Objective: This study aimed to examine the relationship between pretreatment serum LDH levels and factors in advanced solid tumor to find out information for clinical use.
Materials and Methods: This is a cross-sectional study. Data of pretreatment LDH levels in 35 patients with advanced solid tumor at Cancer Clinic, Division of Medical oncology , Department of Internal Medicine, Buddhasothorn Hospital, were collected. And each patient was followed up for 6 months.
Results: The results showed that the pretreatment serum LDH levels did not correlate with factors including age, ECOG performance status, body mass index (BMI), tumor burden, site of metastasis, resection of the primary tumor, received systemic treatment, and 6-month mortality. However, High LDH levels were correlated with liver metastasis and being untreated by systemic treatment with statistical significance.(2-tailed significance, p = 0.001)
Conclusion: Pretreatment serum LDH levels were not found to correlate with the above mentioned factors; nevertheless, High Pretreatment serum LDH level was found to correlate with liver metastasis and correlate with and being untreated by systemic treatment. Data yet had limitations. However, the benefits of this research can be further studied in the future to find a marker that can help to evaluate and follow-up cancer patients.
Keywords: Lactate Dehydrogenase(LDH), Advanced Solid Tumor, Correlation
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive...IJECEIAES
The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
Harnessing Data to Improve Health Equity - Dr. Ali MokdadLauren Johnson
1) The document discusses methods used by the Institute for Health Metrics and Evaluation (IHME) to conduct comprehensive analyses of global, national, and subnational disease burden through their Global Burden of Disease (GBD) study.
2) Key methods discussed include garbage code redistribution to reassign unspecified causes of death, Bayesian meta-regression to estimate incidence and prevalence, and small area statistical models that borrow strength across space, time, and covariates to produce estimates of disease burden for locations with limited data.
3) The GBD study aims to quantify health loss from major diseases, injuries, and risk factors globally and over time in order to help identify and address the world's most pressing health challenges.
Estimating the Survival Function of HIV AIDS Patients using Weibull Modelijtsrd
This work provides information on the survival times of a cohort of infected individuals. The mean survival time was obtained as 22.579 months from the resultant estimate of the shape parameter =1.156 and scale parameter =0.0256 from Weibull 7 simulation of n = 500. Confidence intervals were also obtained for the two parameters at = 0.05 and it was found that the estimates are highly reliable. R. A. Adeleke | O. D. Ogunwale "Estimating the Survival Function of HIV/AIDS Patients using Weibull Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30636.pdf Paper Url :https://www.ijtsrd.com/mathemetics/statistics/30636/estimating-the-survival-function-of-hivaids-patients-using-weibull-model/r-a-adeleke
ABSTRACT
Objective: Stroke is one of the leading causes of death and disabilities worldwide. Cost-effectiveness analysis helps identify neglected opportunities
by highlighting interventions that are relatively inexpensive, yet have the potential to reduce the disease burden substantially. In India, there are
wide social and economic disparities. Socioeconomic environment influences occupation, lifestyle, and nutrition of social classes which in turn would
influence the prevalence and profile of stroke. By reduction of delays in access to hospital and improving provision of affordable treatments can
reduce morbidity and mortality in patients with stroke in India. This study is designed to measure and compare the costs (resources consumed) and
consequences (clinical, economic, and humanistic) of pharmaceutical products and services and their impact on individuals, healthcare systems and
society.
Methods: The purpose of this study is to analyze and conduct a cost-effectiveness analysis for the treatment of stroke in Guntur City Hospitals.
The patients were treated either with aspirin or clopidogrel. The health outcomes were measured using Modified Rankin Scale, A prominent risk
assessment scale for stroke. The pharmacoeconomic data were computed from the patient data collection forms.
Result: The incremental cost-effectiveness ratio of aspirin and clopidogrel were calculated to be Rs. 8046.2/year.
Conclusion: The study concludes that aspirin has the increased socioeconomic impact when compared to Clopidogrel and we can see that the earlier
therapy has supported discharge, home-based rehabilitation along with reduced hospital stay and hence preferable.
Keywords: Stroke, Pharmacoeconomics, Cost-effectiveness analysis, Aspirin, Clopidogrel, Incremental cost-effectiveness ratio.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This study assessed the costs and effects of different degrees of task shifting for anti-retroviral therapy (ART) from physicians to other health professionals in Ethiopia. The study found that (1) facilities with maximal task shifting, where non-physicians performed most ART tasks, had similar patient outcomes and costs as facilities with minimal/moderate task shifting; (2) over 88% of patients remained active on ART after two years across all facility types; and (3) maximal task shifting cost $36 more per patient over two years but resulted in 0.4% fewer patients remaining active, though this difference was not statistically significant.
Machine learning and operations research to find diabetics at risk for readmisison.
A team of researchers was able to apply machine learning to reduce readmissions for diabetics, see "Identifying diabetic patients with high risk of readmission" (Bhuvan,Kumar, Zafar, Aand Kishore, 2016).
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
Improving the performance of k nearest neighbor algorithm for the classificat...IAEME Publication
The document discusses improving the performance of the k-nearest neighbor (kNN) algorithm for classifying diabetes datasets with missing values. It first provides background on diabetes and challenges with missing data. It then describes various data preprocessing techniques used to handle missing values, including mean imputation. The document outlines the kNN classification algorithm and metrics like accuracy and error rate to evaluate performance. It applies these techniques to the Pima Indian diabetes dataset and finds that imputing missing values along with suitable preprocessing like normalization increases classification accuracy compared to ignoring missing values or imputation alone.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Women who test positive for one of the two breast cancer susceptibility genes, BRCA1 and BRCA2, increase their risk by 45-55 percent. Currently, there are no specific physical activity recommendations for these women. However, research supports the positive effect of exercise on reducing breast cancer risk by reducing BMI, adipose tissue, and damage caused by lipid peroxidation.
Data mining is the process of analyzing large databases to discover useful patterns. It involves applying computer-based methods to derive knowledge from large amounts of data. The main components of data mining are knowledge discovery, where concrete information is gleaned from known data, and knowledge prediction, which uses known data to forecast future trends. Data is collected and stored in a centralized data warehouse to allow for easier querying. Common data mining techniques include classification, clustering, regression, and association rule mining. Data mining has various applications in areas such as business, science, medicine, and more to gain useful insights from data. However, effective data mining requires linking multiple data sources which can raise privacy concerns if a person's entire data history is assembled.
This document discusses technological tools for customer relationship management (CRM). It covers the main functionality of CRM applications including sales force automation, campaign management, and customer service and support. Specifically, it describes the functionality required for campaign management like workflow, segmentation, personalization, execution, response management, and response modeling. It also discusses the sales cycle and functionality needed for sales force automation, including interfacing with marketing campaigns and business contact/account management. Finally, it outlines the full customer service cycle from logging requests to billing.
Data mining is a process that analyzes data from different perspectives to discover patterns and relationships. It uses techniques like clustering, regression, and association rules. Clustering groups data into clusters, regression analyzes the statistical relationship between two variables, and association rules are used to analyze sales transactions and customer data. Data mining has various uses including sales/marketing, risk assessment, fraud detection, and customer care. It offers advantages such as being easy to implement, efficiently finding information, increased profitability, and high-quality results.
This document provides an overview of data mining. It introduces data mining and its goals, which include prediction, identification, classification, and optimization. The typical architecture of a data mining system is explained, including its major components. Common data mining techniques like classification, clustering, and association are also outlined. Examples are provided to illustrate techniques. The document concludes by discussing advantages and uses of data mining along with some popular data mining tools.
Data mining involves analyzing large datasets to discover patterns using techniques from machine learning, statistics, and database systems. It is used to extract useful information from large datasets and predict future outcomes. The goal is often predictive analysis to forecast behaviors. The data mining process involves data preparation, model building and validation, and model deployment. Common tools for data mining include neural networks, decision trees, rule induction, genetic algorithms, and nearest neighbor algorithms. While data mining provides benefits like improved marketing and fraud detection, it also raises privacy and security issues regarding personal information.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
Data mining (lecture 1 & 2) conecpts and techniquesSaif Ullah
This document provides an overview of data mining concepts from Chapter 1 of the textbook "Data Mining: Concepts and Techniques". It discusses the motivation for data mining due to increasing data collection, defines data mining as the extraction of useful patterns from large datasets, and outlines some common applications like market analysis, risk management, and fraud detection. It also introduces the key steps in a typical data mining process including data selection, cleaning, mining, and evaluation.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
This document describes a study that uses machine learning techniques to predict heart disease and diabetes from medical data. The study collected data from a public repository and preprocessed it to handle missing values. Feature selection was performed using chi-square and principal component analysis to identify important features. Three boosting classifiers - Adaptive boosting, Gradient boosting, and Extreme Gradient boosting - were trained on the data and evaluated based on accuracy. The results showed that the boosting classifiers achieved accurate prediction for both heart disease and diabetes, with the highest accuracy reported for specific classifiers and diseases.
Performance Evaluation of Data Mining Algorithm on Electronic Health Record o...BRNSSPublicationHubI
This document discusses the performance evaluation of various data mining algorithms on an electronic health record database of diabetic patients. It first provides background on data mining and its applications in healthcare, particularly for diabetes. It then describes the methodology used, which involved preprocessing the data and applying several classification algorithms (decision stump, J48, random forest, neural network, Zero R, One R) to predict diabetes status. The results of each algorithm are evaluated based on accuracy, precision, recall, and error rate. Overall, the document aims to compare the performance of these algorithms on an electronic health record database for diabetes prediction.
DIAGNOSIS OF OBESITY LEVEL BASED ON BAGGING ENSEMBLE CLASSIFIER AND FEATURE S...ijaia
In the current era, the amount of data generated from various device sources and business transactions is
rising exponentially, and the current machine learning techniques are not feasible for handling the massive
volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining
algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction
is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the
initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing
worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is
to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging
algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a
minimal number of feature subsets. We utilized several machine learning algorithms for classifying the
obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on
the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be
promising tools for feature selection in respect of the quality of obtained feature subset and computation
efficiency. Analyzing the results obtained from the original and modified datasets has improved the
classification accuracy and established a relationship between obesity/overweight and common risk factors
such as weight, age, and physical activity patterns.
Metabolic associated fatty liver disease and continuous time Markov chains iman773407
This is dataset pf patients suffering from non-alcoholic fatty liver disease. These data are artificial data to illustrate depiction of the longitudinal study and the statsitical analysis of the results.
Heart disease prediction by using novel optimization algorithm_ A supervised ...BASMAJUMAASALEHALMOH
This document discusses using a novel optimization algorithm called Salp Swarm Optimization (SSO) to predict heart disease. It aims to design a framework for heart disease prediction using major risk factors and different classifier algorithms like Naive Bayes, Support Vector Machine, K-Nearest Neighbors, and a Salp Swarm Optimized Neural Network (SSO-NN). The highest performance was obtained using a Bayesian Optimized Support Vector Machine with 93.3% accuracy, followed by SSO-NN with 86.7% accuracy. The results show that the proposed novel optimized algorithm can provide an effective healthcare monitoring system for early heart disease prediction.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
An Empirical Study On Diabetes Mellitus Prediction For Typical And Non-Typica...Scott Faria
The document presents a study that uses machine learning approaches to predict diabetes for both typical and non-typical cases. Three machine learning algorithms (Bagging, Logistic Regression, Random Forest) were applied to a dataset of 340 patients with 26 features, and their accuracy was measured. Random Forest performed best with an accuracy of 90.29%, followed by Bagging at 89.12% and Logistic Regression at 83.24%.
IRJET- Comparison of Techniques for Diabetes Detection in Females using Machi...IRJET Journal
This document discusses and compares different machine learning techniques for detecting diabetes in females using various factors. It analyzes logistic regression and decision tree algorithms on a dataset containing factors like pregnancies, glucose levels, blood pressure, skin thickness, BMI, age and outcomes. Logistic regression is used to predict the binary outcome of having diabetes. Decision trees divide the problem into smaller subsets and use entropy and information gain to predict the most distinguishing parameters. The techniques are compared to prior studies on diabetes detection using machine learning.
This document discusses a systematic review and meta-analysis on the relationship between dietary fat intake and breast cancer risk. The meta-analysis included 45 studies with over 25,000 breast cancer patients. It found a small increased risk of breast cancer associated with higher total fat intake. The review also discusses terms related to systematic reviews and meta-analysis such as heterogeneity statistics, I2, and the Q statistic.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
This document summarizes a research paper that developed an artificial intelligence system using the K-nearest neighbors algorithm to diagnose hyperglycemia (high blood sugar). The system was trained on a database of 415 patient cases characterized by 10 physiological parameters. It achieved a diagnostic accuracy of 91% compared to medical experts when tested on new patient data. The authors conclude the KNN-based system is useful for diabetes diagnosis and could help supplement medical doctors, especially in remote areas with limited access to experts.
This study conducted a systematic review and meta-analysis of bariatric surgery outcomes using data from 164 studies published between 2003-2012 including over 161,000 patients. The analysis found that bariatric surgery provides substantial and sustained weight loss and reduction in obesity-related health conditions, though risks of complications, reoperations, and death do exist. Specifically, the 30-day mortality rate was 0.08% and the rate after 30 days was 0.31%. The complication rate was 17% and the reoperation rate was 7%. Greater weight loss was seen with gastric bypass but it had higher complication rates than adjustable gastric banding or sleeve gastrectomy.
HEART DISEASE PREDICTION USING MACHINE LEARNING AND DEEP LEARNINGIJDKP
Heart disease is most common disease reported currently in the United States among both the genders and
according to official statistics about fifty percent of the American population is suffering from some form of
cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict
heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors
to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of
disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart
disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results
and analysis show that males in the United States are most likely to develop heart disease with the
symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that
there is a week corelation of 0.5 is identified which shows people with all ages including teens can face
heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the
participant who are facing severe chest pain is suffering from heart disease where majority of the
successful heart disease identified is in males and only 10 percent participants are identified as healthy.
The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like
sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in
onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic
regression and observed an accuracy of 85.12 percent.
Genetically Optimized Neural Network for Heart Disease ClassificationIRJET Journal
This document describes a study that uses a genetically optimized neural network to classify heart disease based on patient risk factors. The study collects data on 12 risk factors from 50 patients and encodes the values for use as input to a neural network. The neural network is initially trained using backpropagation, then genetic algorithms are used to optimize the network weights and biases to improve accuracy. Confusion matrices are plotted to evaluate the accuracy of the optimized neural network at classifying patients as having heart disease or not. The approach achieves a classification accuracy of 90% on the test data.
La Hipertensión, es una de las mayores enfermedades que sufren los Hispanohablantes en el planeta . Es grato poder colocar este documento al público y haber podido hacer parte del equipo , ojalá sirvan a muchos las implementaciones. idioma más hablado según el foro Económico mundial - Me refiero al español ó castellano según sea -
segundo idioma y haber podido hacer parte de este equipo. Genuinamente, espero que se curen la mayor cantidad de personas con . Espero genuinamente puedan hacer algúna donación a este esfuerzo grupal. Espero Compartamos este "Paper" así como compartimos memes - En el sentido literal de la significancia-
** Refierase a Wikipedia sino tiene un diccionario a mano.
Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm...IRJET Journal
This document discusses several machine learning algorithms that have been used to predict diabetes, including KNN, Naive Bayes, Random Forest, J48, SVM, logistic regression, decision trees, neural networks, and ensemble models. It analyzes past research applying these methods to diabetes prediction and reports their accuracy results. The document then proposes using an ensemble hybrid model combining KNN, Naive Bayes, Random Forest, and J48 algorithms to predict diabetes with increased performance and accuracy compared to individual techniques.
Data Science Meets Healthcare: The Advent of Personalized Medicine - Jacomo C...CityAge
Healthcare spending is growing unsustainably as a proportion of GDP. The advent of big data and personalized medicine enabled by electronic health records, rich sensor data from devices, and advances in machine learning algorithms provides an opportunity to make healthcare more efficient and effective. Two case studies are described: 1) Using machine learning to develop more targeted preventive screening policies that balance benefits and harms better than current demographic-based guidelines. 2) Analyzing surgical team performance data to optimize team assignment and forecast outcomes, finding experience factors like dyadic team experience matter more than conventional views of individual experience alone. Effective use of data requires asking the right questions, joining diverse data sources, iterative testing, and a focus on real-world impact.
iHealth2016 Submission-VA Diabetes Risk AssessmentGanesh N Prasad
This document discusses an approach to using statistical machine learning and information visualization techniques to assess patients' risk of diabetes complications. The approach was evaluated on data from over 8,600 patients. Key results include:
- A two-dimensional visualization was able to stratify patients into high- and low-risk groups for heart attack based on relevant risk factors like smoking and BMI.
- The tool allows exploring risk predictions at both the population level and for individual patients. It also shows the potential impact of interventions on modifying patient risk levels.
- An integrated risk assessment and visualization tool could benefit clinicians, patients, and other stakeholders by facilitating risk prediction, stratification, and exploring optimal treatments in a personalized way.
Similar to Summarization Techniques in Association Rule Data Mining For Risk Assessment of Diabetes Mellitus (20)
Beaglebone Black Webcam Server For SecurityIJTET Journal
Web server security using BeagleBone Black is based on ARM Cortex-A8 processor and Linux operating system
is designed and implemented. In this project the server side consists of BeagleBone Black with angstrom OS and interfaced
with webcam. The client can access the web server by proper authentication. The web server displays the web page forms
like home, video, upload, settings and about. The home web page describes the functions of Web Pages. The video Web page
displays the saved videos in the server and client can view or download the videos. The upload web page is used by the client
to upload the files to server. The settings web page is used to change the username, password and date if needed. The about web page provides the description of the project
Biometrics Authentication Using Raspberry PiIJTET Journal
This document discusses a biometrics authentication system using fingerprint recognition on a Raspberry Pi. It uses a fingerprint reader module connected to a Raspberry Pi. Fingerprint images are captured using a GUI application and converted to binary templates. The templates are stored in a PostgreSQL database. A Python script is used to match fingerprints by comparing templates and identifying matching ridge patterns between fingerprints. The system was able to accurately match fingerprints from the same finger and distinguish fingerprints from different fingers based on the ridge patterns. Future work involves improving the matching accuracy and developing the system for real-time high-end applications.
Conceal Traffic Pattern Discovery from Revealing Form of Ad Hoc NetworksIJTET Journal
Number of techniques has been planned supported packet secret writing to safeguard the
communication in MANETs. STARS functioning supported stastical characteristics of captured raw traffic.
STARS discover the relationships of offer to destination communication. To forestall STAR attack associate
offer hidding technique is introduced.The pattern aims to derive the source/destination probability distribution.
that's the probability for each node to entire traffic captured with link details message source/destination and
conjointly the end-to-end link probability distribution that's the probability for each strive of nodes to be
associate end-to-end communication strive. thence construct point-to-point traffic originate and then derive the
end-to-end traffic with a set of traffic filtering rules; thus actual traffic protected against revelation attack.
Through this protective mechanism efficiency of traffic enlarged by ninety fifth from attacked traffic. For a lot of
sweetening to avoid overall attacks second shortest path is chosen.
Node Failure Prevention by Using Energy Efficient Routing In Wireless Sensor ...IJTET Journal
The most necessary issue that has to be solved in coming up with an information transmission rule for
wireless unplanned networks is a way to save unplanned node energy whereas meeting the wants of applications
users because the unplanned nodes are battery restricted. Whereas satisfying the energy saving demand, it’s
conjointly necessary to realize the standard of service. Just in case of emergency work, it's necessary to deliver the
information on time. Achieving quality of service in is additionally necessary. So as to realize this demand, Power -
efficient Energy-Aware routing protocol for wireless unplanned networks is planned that saves the energy by
expeditiously choosing the energy economical path within the routing method. When supply finds route to
destination, it calculates α for every route. The worth α is predicated on largest minimum residual energy of the trail
and hop count of the trail. If a route has higher α, then that path is chosen for routing the information. The worth of α
are higher, if the most important of minimum residual energy of the trail is higher and also the range of hop count is
lower. Once the trail is chosen, knowledge is transferred on the trail. So as to extend the energy potency any
transmission power of the nodes is additionally adjusted supported the situation of their neighbour. If the neighbours
of a node are closely placed thereto node, then transmission vary of the node is diminished. Thus it's enough for the
node to own the transmission power to achieve the neighbour at intervals that vary. As a result transmission power
of the node is cut back that later on reduces the energy consumption of the node. Our planned work is simulated
through Network machine (NS-2). Existing AODV and Man-Min energy routing protocol conjointly simulated
through NS-2 for performance comparison. Packet Delivery quantitative relation, Energy Consumption and end-toend
delay.
Prevention of Malicious Nodes and Attacks in Manets Using Trust worthy MethodIJTET Journal
In Manet the first demand is co-operative communication among nodes. The malicious nodes might cause security issues like grey hole and cooperative attacks. To resolve these attack issue planning Dynamic supply routing mechanism, that is referred as cooperative bait detection theme (CBDS) that integrate the advantage of each proactive and reactive defence design is used. In region attacks, a node transmits a malicious broadcast informing that it's the shortest path to the destination, with the goal of intercepting messages. During this case, a malicious node (so-called region node) will attract all packets by victimisation solid Route Reply (RREP) packet to incorrectly claim that “fake” shortest route to the destination then discard these packets while not forwarding them to the destination. In grey hole attacks, the malicious node isn't abs initio recognized in and of itself since it turns malicious solely at a later time, preventing a trust-based security resolution from detective work its presence within the network. It then by selection discards/forwards the info packets once packets undergo it. During this we have a tendency to focus is on detective work grey hole/collaborative region attacks employing a dynamic supply routing (DSR)-based routing technique.
Effective Pipeline Monitoring Technology in Wireless Sensor NetworksIJTET Journal
Wireless detector nodes are a promising technology to play three-dimensional applications. Even it
will sight correct lead to could on top of ground and underground. In solid underground watching system makes
some challenges are there to propagating the signals. The detector node is moving entire the underground
pipeline and sending information to relay node that's placed within the on top of ground. If any relay node is
unsuccessful during this condition suggests that it'll not sending the info. In this watching system can specially
designed as a heterogeneous networks. Every high power relay nodes most covers minimum 2 low power relay
node. If any relay node is unsuccessful within the network, the constellation can modification mechanically
supported the heterogeneous network. The high power relay node is change the unsuccessful node and sending
the condition of pipeline. The benefits are thought-about to be extremely distributed, improved packet delivery
Raspberry Pi Based Client-Server Synchronization Using GPRSIJTET Journal
A low cost Internet-based attendance record embedded system for students which uses wireless technology to
transfer data between the client and server is designed. The proposed system consist of a Raspberry Pi which acts as a
client which stores the details of the students in the database by using user login system using web. When the user logs
into the database the data is sent through GPRS to the server machine which maintains the records of the employees and
the attendance is updated in the server database. The GPRS module provides a bidirectional real-time data transfer
between the client and server. This system can be implemented to any real time application so as to retrieve information
from a data source of the client system and send a file to the remote server through GPRS. The main aim is to avoid the
limitations in Ethernet connection and design a low cost and efficient attendance record system where the data is
transferred in a secure way from the client database and updated in the server database using GPRS technology
ECG Steganography and Hash Function Based Privacy Protection of Patients Medi...IJTET Journal
Data hiding can hide sensitive information into signals for covert communication. Most data hiding
techniques will distort the signal in order to insert additional messages. The distortion is often small; the irreversibility is
not admissible to some sensitive techniques. Most of the applications, lossless data hiding is desired to extract the
embedded data and the original host signal. The project proposes the enhancement of protection system for secret data
communication through encrypted data concealment in ECG signals of the patient. The proposed encryption technique
used to encrypt the confidential data into unreadable form and not only enhances the safety of secret carrier information by
making the information inaccessible to any intruder having a random method. For that we use twelve square ciphering
techniques. The technique is used make the communication between the sender and the receiver to be authenticated is hash
function. To evaluate the effectiveness of ECG wave at the proposed technique, distortion measurement techniques of two
are used, the percentage residue difference (PWD) and wavelets weighted PRD. Proposed technique provides high security protection for patient data with low distortion is proven in this proposed system.
An Efficient Decoding Algorithm for Concatenated Turbo-Crc CodesIJTET Journal
In this paper, a hybrid turbo decoding algorithm is used, in which the outer code, Cyclic Redundancy Check code is
not used for detection of errors as usual but for error correction and improvement. This algorithm effectively combines the iterative
decoding algorithm with Rate-Compatible Insertion Convolution Turbo Decoding, where the CRC code and the turbo code are
regarded as an integrated whole in the Decoding process. Altogether we propose an effective error detecting method based on
normalized Euclidean distance to compensate for the loss of error detection capability which should have been provided by CRC
code. Simulation results show that with the proposed approach, 0.5-2dB performance gain can be achieved for the code blocks
with short information length
Improved Trans-Z-source Inverter for Automobile ApplicationIJTET Journal
In this paper a new technology is proposed with a replacement of conventional voltage source/current
source inverter with Improved Trans-Z-source inverter in automobile applications. The improved Trans-Z-source
inverter has a high boost inversion capability and continues input current. Also this new inverter can suppress the
resonant current at the startup; this resonant current in the startup may lead the device to permanent damage. In
improved Trans-Z-source inverter a couple inductor is needed, instead of this coupled inductor a transformer is used.
By using a transformer with sufficient turns ratio the size can be reduced. The turn’s ratio of the transformer decides
the input voltage of the inverter. In this paper operating principle, comparison with conventional inverters, working
with automobiles simulation results, THD analysis, Hardware implementation using ATMEGA 328 P are included.
Wind Energy Conversion System Using PMSG with T-Source Three Phase Matrix Con...IJTET Journal
This document presents a wind energy conversion system using a permanent magnet synchronous generator (PMSG) connected to a T-source three-phase matrix converter. The system aims to efficiently harness wind power and deliver it to a load. A PMSG is connected to a three-phase diode rectifier and input capacitors, with the output fed to a T-source network and three-phase matrix converter. The converter can boost output voltage regardless of input voltage and regulate it through shoot-through control. MATLAB/Simulink models are developed and simulations show the converter produces controlled output voltage and current waveforms to power the load efficiently with fewer components than traditional converter topologies.
Comprehensive Path Quality Measurement in Wireless Sensor NetworksIJTET Journal
A wireless sensor network mostly relies on multi-hop transmissions to deliver a data packet. It is of essential importance to measure the quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. Existing metrics like ETF, ETX mainly focus on quantifying the link performance in between the nodes while overlooking the forwarding capabilities inside the sensor nodes. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multihop routing protocols. We propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. We implement QoF and build a modified Collection Tree Protocol.
Optimizing Data Confidentiality using Integrated Multi Query ServicesIJTET Journal
Query services have experienced terribly massive growth within past few years for that reason large usage of services need to balance outsourcing data management to Cloud service providers that provide query services to the client for data owners, therefore data owner needs data confidentiality as well as query privacy to be guaranteed attributable to disloyal behavior of cloud service provider consequently enhancing data confidentiality must not be compromise the query processed performance. It is not significant to provide slow query services as the result of security along with privacy assurance. We propose the random space perturbation data perturbation method to provide secure with kNN(k-nearest-neighbor) range query services for protecting data in the cloud and Frequency Structured R-Tree (FSR-Tree) efficient range query. Our schemes enhance data confidentiality without compromising the FSR-TREE query processing performance that also increases the user experience.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
Harmonic Mitigation Method for the DC-AC Converter in a Single Phase SystemIJTET Journal
This document summarizes a research paper that proposes a harmonic mitigation method for a DC-AC converter without using a low pass filter. Specifically, it suggests using sine wave modulation of the converter along with injection of specific harmonics calculated using Fourier analysis to cancel out existing harmonics. A proportional-resonant integral controller is also used to eliminate any DC offset. Simulation results show the total harmonic distortion is reduced to 11.15% using this approach, avoiding the need for an output filter. The proposed method continuously monitors and mitigates harmonics in the output to improve power quality.
Comparative Study on NDCT with Different Shell Supporting StructuresIJTET Journal
Natural draft cooling towers are very essential in modern days in thermal and nuclear power stations. These are the hyperbolic shells of revolution in form and are supported on inclined columns. Several types of shell supporting structures such as A,V,X,Y are being used for construction of NDCT’s. Wind loading on NDCT governs critical cases and requires attention. In this paper a comparative study on reinforcement details has been done on NDCT’s with X and Y shell supporting structures. For this purpose 166m cooling tower with X and Y supporting structures being analyzed and design for wind (BS & IS code methods), seismic loads using SAP2000.
Experimental Investigation of Lateral Pressure on Vertical Formwork Systems u...IJTET Journal
The modeling of pressure distribution of fresh concrete poured in vertical formwork are rather dynamic than complex. Many researchers had worked on the pressure distribution modeling of concrete and formulated empirical relationship factors like formwork height, rate of pour, consistency classes of concrete. However, in the current scenario, most of high rise construction uses self compacting concrete(SCC) which is a special concrete which utilizes not only mineral and chemical admixtures but also varied aggregate proportions and hence modeling pressure distribution of SCC over other concrete in vertical formwork systems is necessitated. This research seeks to bridge the gap between the theoretical formulation of pressure distribution with the actual modeled (scaled) vertical formwork systems. The pressure distribution of SCC in the laboratory will be determined using pressure sensors, modeled and analyzed.
A Five – Level Integrated AC – DC ConverterIJTET Journal
This paper presents the implementation of a new five – level integrated AC – DC converter with high input power factor and reduced input current harmonics complied with IEC1000-3-2 harmonic standards for electrical equipments. The proposed topology is a combination of boost input power factor pre – regulator and five – level DC – DC converter. The single – stage PFC (SSPFC) approach used in this topology is an alternative solution to low – power and cost – effective applications.
A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein an...IJTET Journal
Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and authentication systems.
Study of Eccentrically Braced Outrigger Frame under Seismic ExitationIJTET Journal
Outrigger braced structures has efficient structural form consist of a central core, comprising braced frames with
horizontal cantilever ”outrigger” trusses or girders connecting the core to the outer column. When the structure is loaded
horizontally, vertical plane rotation of the core is restrained by the outriggers through tension in windward column and
compression in leeward column. The effective structural depth of the building is greatly increased, thus augmenting the lateral
stiffness of the building and reducing the lateral deflections and moments in core. In effect, the outriggers join the columns to the
core to make the structure behave as a partly composite cantilever. By providing eccentrically braced system in outrigger frame by
varying the size of links and analyzing it. Push over analysis is carried out by varying the link size using computer programs, Sap
2007 to understand their seismic performance. The ductile behavior of eccentrically braced frame is highly desirable for structures
subjected to strong ground motion. Maximum stiffness, strength, ductility and energy dissipation capacity are provided by
eccentrically braced frame. Studies were conducted on the use of outrigger frame for the high steel building subjected to
earthquake load. Braces are designed not to buckle, regardless of the severity of lateral loading on the frame. Thus eccentrically
braced frame ensures safety against collapse.
How to Show Sample Data in Tree and Kanban View in Odoo 17Celine George
In Odoo 17, sample data serves as a valuable resource for users seeking to familiarize themselves with the functionalities and capabilities of the software prior to integrating their own information. In this slide we are going to discuss about how to show sample data to a tree view and a kanban view.
Still I Rise by Maya Angelou
-Table of Contents
● Questions to be Addressed
● Introduction
● About the Author
● Analysis
● Key Literary Devices Used in the Poem
1. Simile
2. Metaphor
3. Repetition
4. Rhetorical Question
5. Structure and Form
6. Imagery
7. Symbolism
● Conclusion
● References
-Questions to be Addressed
1. How does the meaning of the poem evolve as we progress through each stanza?
2. How do similes and metaphors enhance the imagery in "Still I Rise"?
3. What effect does the repetition of certain phrases have on the overall tone of the poem?
4. How does Maya Angelou use symbolism to convey her message of resilience and empowerment?
How to Install Theme in the Odoo 17 ERPCeline George
With Odoo, we can select from a wide selection of attractive themes. Many excellent ones are free to use, while some require payment. Putting an Odoo theme in the Odoo module directory on our server, downloading the theme, and then installing it is a simple process.
How to Add Colour Kanban Records in Odoo 17 NotebookCeline George
In Odoo 17, you can enhance the visual appearance of your Kanban view by adding color-coded records using the Notebook feature. This allows you to categorize and distinguish between different types of records based on specific criteria. By adding colors, you can quickly identify and prioritize tasks or items, improving organization and efficiency within your workflow.
Principles of Roods Approach!!!!!!!.pptxibtesaam huma
Principles of Rood’s Approach
Treatment technique used in physiotherapy for neurological patients which aids them to recover and improve quality of life
Facilitatory techniques
Inhibitory techniques
How to Store Data on the Odoo 17 WebsiteCeline George
Here we are going to discuss how to store data in Odoo 17 Website.
It includes defining a model with few fields in it. Add demo data into the model using data directory. Also using a controller, pass the values into the template while rendering it and display the values in the website.
How to Configure Time Off Types in Odoo 17Celine George
Now we can take look into how to configure time off types in odoo 17 through this slide. Time-off types are used to grant or request different types of leave. Only then the authorities will have a clear view or a clear understanding of what kind of leave the employee is taking.
Split Shifts From Gantt View in the Odoo 17Celine George
Odoo allows users to split long shifts into multiple segments directly from the Gantt view.Each segment retains details of the original shift, such as employee assignment, start time, end time, and specific tasks or descriptions.
Beyond the Advance Presentation for By the Book 9John Rodzvilla
In June 2020, L.L. McKinney, a Black author of young adult novels, began the #publishingpaidme hashtag to create a discussion on how the publishing industry treats Black authors: “what they’re paid. What the marketing is. How the books are treated. How one Black book not reaching its parameters casts a shadow on all Black books and all Black authors, and that’s not the same for our white counterparts.” (Grady 2020) McKinney’s call resulted in an online discussion across 65,000 tweets between authors of all races and the creation of a Google spreadsheet that collected information on over 2,000 titles.
While the conversation was originally meant to discuss the ethical value of book publishing, it became an economic assessment by authors of how publishers treated authors of color and women authors without a full analysis of the data collected. This paper would present the data collected from relevant tweets and the Google database to show not only the range of advances among participating authors split out by their race, gender, sexual orientation and the genre of their work, but also the publishers’ treatment of their titles in terms of deal announcements and pre-pub attention in industry publications. The paper is based on a multi-year project of cleaning and evaluating the collected data to assess what it reveals about the habits and strategies of American publishers in acquiring and promoting titles from a diverse group of authors across the literary, non-fiction, children’s, mystery, romance, and SFF genres.
Satta Matka Dpboss Kalyan Matka Results Kalyan ChartMohit Tripathi
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Beginner's Guide to Bypassing Falco Container Runtime Security in Kubernetes ...anjaliinfosec
This presentation, crafted for the Kubernetes Village at BSides Bangalore 2024, delves into the essentials of bypassing Falco, a leading container runtime security solution in Kubernetes. Tailored for beginners, it covers fundamental concepts, practical techniques, and real-world examples to help you understand and navigate Falco's security mechanisms effectively. Ideal for developers, security professionals, and tech enthusiasts eager to enhance their expertise in Kubernetes security and container runtime defenses.