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2011 24th International Symposium on Computer-Based Medical Systems (CBMS), 2011
Extensive amounts of data stored in medical databases require the development of dedicated tools for accessing the data, data analysis, knowledge discovery, and effective use of sloretl knowledge and data. Widespread use of medical information systems and explosive enlargement of medical databases require conventional manual data analysis to be coupled with methods for competent computer-assisted analysis. In this paper, I use Data Mining techniques for the data analysis, data accessing and knowledge discovery procedure to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained outcome will be tested by applying the approach to the databases, data warehouses and any data storage of different sizes with different entry values. The results shaped will be of different level from short to the largest sets of tuple. By this, we may take the results formed for different use e.g. Patient investigation, frequency of different disease.
2013 24th International Workshop on Database and Expert Systems Applications, 2013
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/a-review-on-data-mining-techniques-and-challenges-in-medical-field https://www.ijert.org/research/a-review-on-data-mining-techniques-and-challenges-in-medical-field-IJERTV9IS080143.pdf The healthcare industry has witnessed an enormous evolution in producing huge amounts of medical data that have given rise to research in multiple areas. Many researchers reviewed and surveyed the healthcare, which is an active interdisciplinary field of data mining. Technological advances in information on health care, digitizing health records, have resulted in rapid growth of the healthcare sector. Electronic Health Record Systems (EHRs) are the data repositories which are the digitized format for the medical data storage. Healthcare sector manages enormous amounts of data that needs to be analyzed to provide a better solution for better decision making. The main challenge is how to use the data mining techniques to effectively discover useful and important information among the massive amount of data available. It plays a major role in the advancement and development of new techniques that work effectively for the huge data in healthcare. The related information is collected that demonstrates the importance of data mining in health care. This paper mainly focuses on the necessity of data mining in medical field, its applications in health sector, different predictive and descriptive data mining techniques that can be used in various applications of healthcare sector and challenges that are involved in mining the health data.
International Statistical Review, 2019
American Journal of Health Behavior, 2001
Journal of health informatics, 2013
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/survey-on-predictive-medical-data-analysis https://www.ijert.org/research/survey-on-predictive-medical-data-analysis-IJERTV3IS042253.pdf The medical field is one of the most important industries that can benefit vastly from the many advantages of Cloud Computing and data mining tools. The system we propose combines both domains by enabling doctors, patients, pharmacists, pharmaceutical companies to find hidden trends in medical data (EMRs) stored on a cloud and thus, predict trends for the future. The system is to be supported by a knowledge base that will store recorded data and enhance predictions. The dataset consists of the attributes namely: age, gender, region, climate, time period, diseases and respective diagnosis. A user of the system can find patterns of diseases under one of the categories: age, gender, region and time period. The proposed system explores the application of the k-means algorithm to cluster the data and tests for any modification of the algorithm that maybe required for producing more efficient and accurate results..
2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) , 2020
Health data analysis can discover the secret trends in health field variables with enormous promise. Such variations can also be used to identify medically. Furthermore, unprocessed, large-scale, disparate, and weighty clinical data can be found. The information must be compiled in a systematic way. Such information can be further merged into a patient data system. Big data software presents a user-focused appearance to fresh including unknown information trends. The processing of statistical information is targeted at processing the information and mechanisms of software. The results are diverse, while the big data system is monolithic. They describe a variety of fields in which these methods can be used for information exploration of health systems. Throughout this article, we discuss momentarily the effect of data analysis methods to clinical imaging, like computational models.
Revista de Cultura Teológica. ISSN (impresso) 0104-0529 (eletrônico) 2317-4307, 2013
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Yoga Sudha, August , 2023
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