Comparison of Coronary Heart Disease Prediction models using various Machine Learning Algorithms

Abstract

In the health sector, Data Analytics and Machine Learning (ML) methods are taking over role of skill and experience of a doctor especially in diagnosing diseases and preventive health measures. The health care industry is collecting very large amount of data related to patients, his medical history for preventive medication and diagnosing disease well in time and more accurately.  In this paper, a comparison of five classification machine learning methods viz. Decision Tree, Random Forests, Support Vector Machine, Artificial Neural Network and Fuzzy Logic based soft computing method is done for heart disease diagnosis on the basis of data available on public domain.  Out of 76 parameters collected for a patient, only 15 medical parameters such as blood pressure, sex, age, obesity and cholesterol level are used for predicting heart disease of patients.