A SURVEY AND ANALYSIS ON HEART DISORDERS USING NAVE BAYES

  • ASHLEY DENIS et al.

Abstract

Today's health-care to provide medical care to the patients and protect them from various diseases. This comprises the development of a framework based on associative classification techniques on heart dataset for early diagnosis of heart based diseases. It is hard to diagnose the heart diseases with just observation that arrives suddenly and may prove fatal when its uncontrolled. The implementation of work is done on heart diseases dataset from kaggle (UCI) machine learning repository to test on different data mining techniques. The various attributes related to cause of heart diseases are viz: gender, age, chest pain type, blood pressure, blood sugar etc that can predict early symptoms heart disease. Various data mining algorithms such as Aprior, FP-Growth, Naive bayes, ZeroR, OneR, J48 and k-nearest neighbour are applied in this study for prediction of heart diseases. On basis of best results the development of heart disease prediction system is done by using hybrid technique for classification associative rules to achieve the prediction accuracy of 99.19%.

Published
2019-12-08