A Generalized Model for Cardiovascular Disease Classification Using Machine Learning Techniques

Abstract

The number of deaths caused due to cardiovascular ailments is increasing day by day. Prediction of such deadly disease is an unwieldy job for the medical practitioners as it needs adequate experience and knowledge. This study focuses on the real problem dealing with heart patient’s data to make well-timed detection and prognosis of risk. The research revolves around building a prediction model to diagnose coronary heart disease. For this purpose, ensemble techniques are tried out along with eight standard classifiers. Two medical datasets are used. In this paper, standard methods are tried out to find the best technique that works for the two datasets chosen. The best results on two datasets are reported for each method.