COMPARATIVE STUDY ON MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

Abstract

Heart disease is one of the most critical human diseases in the world and affects human life to a very large extent. An accurate and timely diagnosis of heart disease is important to treat and prevent a heart failure. Using machine learning techniques and the data procured by the health care industry, a disease can be detected, predicted and even cured. In this paper, the Naive Bayes, Linear Classifier, K-nearest Neighbour and Random Forest machine learning algorithms have been applied. The results of these four algorithms were compared on the basis of accuracy, specificity and sensitivity for prediction of disease.