International journal of computer applications, Sep 24, 2022
Student admission's process is a method of selecting qualified candidates for admission. ... more Student admission's process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for postsecondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for postsecondary education based on (O'level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC-ROC and gave a 99% accuracy on the dataset.
European Journal of Computer Science and Information Technology
Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD... more Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and ECG datasets. A quantitative research approach was used for the study with examination of several segments based on recommended framework. Three (3) classifier models were adopted to detect cardiac related problems using specified datasets. The classifiers such as; Random Forest Ensemble (RFE), Support Vector (SVM) Classifier and Artificial Neural Network (ANN) was employed for Machine Learning...
Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar ma... more Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar management. There is a strong relationship between a person's FBS level and cardiovascular disease (CVD) because uncontrolled long-term high FBS level can lead to CVD. Devising a means of predicting Fasting blood Sugar level of a patient will go a long way in proper management of diabetes and in turn help in cardiovascular disease control. Predicting the level of FBS for purposes of controlling CVD is the aim of this research. An all-inclusive review was first carried out on Fasting Blood Sugar, Blood Glucose Test, Diabetes, Cardiovascular disease and Machine Learning. Secondly, General Logistic Model (GLM) was adopted for the prediction of Fasting Blood Sugar levels based on the metrics used. Performance analysis results show effective prediction using the Confusion Matrix and AUC-ROC which gave 70% accuracy on the dataset used. Thirdly, the logistic regression model was deployed to Application Programming Interface (API) where each medical practitioner can adopt and used for predicting patient's blood sugar level based on the metrics provided.
Student admission's process is a method of selecting qualified candidates for admission. Challeng... more Student admission's process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for postsecondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for postsecondary education based on (O'level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC-ROC and gave a 99% accuracy on the dataset.
International journal of computer applications, Sep 24, 2022
Student admission's process is a method of selecting qualified candidates for admission. ... more Student admission's process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for postsecondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for postsecondary education based on (O'level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC-ROC and gave a 99% accuracy on the dataset.
European Journal of Computer Science and Information Technology
Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD... more Most disease that affects the heart or blood vessels is referred to as cardiovascular disease(CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and ECG datasets. A quantitative research approach was used for the study with examination of several segments based on recommended framework. Three (3) classifier models were adopted to detect cardiac related problems using specified datasets. The classifiers such as; Random Forest Ensemble (RFE), Support Vector (SVM) Classifier and Artificial Neural Network (ANN) was employed for Machine Learning...
Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar ma... more Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar management. There is a strong relationship between a person's FBS level and cardiovascular disease (CVD) because uncontrolled long-term high FBS level can lead to CVD. Devising a means of predicting Fasting blood Sugar level of a patient will go a long way in proper management of diabetes and in turn help in cardiovascular disease control. Predicting the level of FBS for purposes of controlling CVD is the aim of this research. An all-inclusive review was first carried out on Fasting Blood Sugar, Blood Glucose Test, Diabetes, Cardiovascular disease and Machine Learning. Secondly, General Logistic Model (GLM) was adopted for the prediction of Fasting Blood Sugar levels based on the metrics used. Performance analysis results show effective prediction using the Confusion Matrix and AUC-ROC which gave 70% accuracy on the dataset used. Thirdly, the logistic regression model was deployed to Application Programming Interface (API) where each medical practitioner can adopt and used for predicting patient's blood sugar level based on the metrics provided.
Student admission's process is a method of selecting qualified candidates for admission. Challeng... more Student admission's process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for postsecondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for postsecondary education based on (O'level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC-ROC and gave a 99% accuracy on the dataset.
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