HEART DISEASE PREDICTION USING VARIOUS MACHINES LEARNING APPROACH

Abstract

In health sector computer aided diagnosis (CAD) system is rapidly growing area because medical
diagnostic systems make huge change as compare to traditional system. Now a day huge availability of
medical data and it need proper system to extract them in to useful knowledge. Machine learning has been
exposed to be operative in supporting in making decision and predication from the large quantity of data
produce by the healthcare sector. Classification is a prevailing machine leaning approach which are
commonly used for predication some classification algorithm predict accurate result according to the marks
whereas some others exhibit a limited accuracy. So in this paper investigates some of classification
approaches and also combining multiple classifiers for single approaches are used. A reasonable analytical
of these technique was done to conclude how the cooperative techniques can be applied for improving
prediction accuracy in heart disease. Five main classifiers used to construct heart disease prediction base on
the experimental results demonstrate that support vector machine, naive bayes, logistic regression, decision
tree and memory-based learner provide reliable and accurate result.