Optimal Machine Learning Classifiers for Prediction of Heart Disease

Abstract

Cardiovascular Disease (CVDs) is the major cause of mortality. Heart disease is one of the serious and prevalent diseases which is impacting millions of people across the globe and needs special attention and cure. Many researchers are working together to find feasible solution for prediction of heart disease and for that they are involving popular technology i.e. Artificial Intelligence (AI) and Machine Learning (ML). There are many popular classifiers in machine learning like Decision Tree (DT), Support Vector Machine, Naive Bayes (NB), KNN, Logistic Regression (LR), Artificial Neural Network, Deep Neural Network, and many more which has been used by researchers to find a solution for many health related disease. In this paper, an experiment has been conducted for prediction of heart disease using popular classification models like SVM, DT, NB, Random Forest, DNN and KNN and to find best model suitable for this purpose using ML.