TO PREDICT THE CHANCE OF HEART ATTACK USING LOGISTIC REGRESSION AND PERFORMANCE IMPROVEMENT USING DIFFERENT CLASSIFIERS

Authors

  • Vaishali Shinde JJTU University, Rajasthan Dept of Computer Scinece
  • Dr. Yogesh Kumar Sharma JJTU University, Rajasthan Dept of Computer Scinece
  • Harish Barapatre JJTU University, Rajasthan Dept of Computer Scinece

DOI:

https://doi.org/10.17605/OSF.IO/6QJ5Z

Keywords:

Data Mining, Student Database Mapping, Prediction analysis, Educational dynamics of behavior

Abstract

Educational Data Mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyse educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. Firstly, it introduces EDM and describes the different groups of users, types of educational environments and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data mining techniques and finally some of the most promising future lines of research are discussed. With the overwhelming successes gained in Big Data analysis in the Business Industry, it is little wonder that there is a strong belief in the academia that these successes can be replicated in the Education Sector. As new findings and outcomes of research crop up daily, it is my belief that amongst these successes potentially identifiable, prediction of students’ academic performance can have strong positive influences in knowledge management and delivery in education thereby adding more quality to the learning experience.

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Published

10-11-2021

How to Cite

[1]
Vaishali Shinde, Dr. Yogesh Kumar Sharma, and Harish Barapatre, “TO PREDICT THE CHANCE OF HEART ATTACK USING LOGISTIC REGRESSION AND PERFORMANCE IMPROVEMENT USING DIFFERENT CLASSIFIERS”, IEJRD - International Multidisciplinary Journal, vol. 6, no. ICRRTNB, p. 11, Nov. 2021.