Heart disease Prediction using Machine Learning

Abstract

Heart disease is among the main causes of fatalities worldwide, in our days. However, early detection of cardiac problems and timely care by health practitioners can reduce the mortality rate. Therefore, a reliable system for assessing such pathologies is of utmost importance to be able to process an adequate treatment. In this paper, we investigate various classification techniques to timely diagnose persons registered to receive medical treatment who are suffering from heart malfunctions. Accordingly, we can proactively identify issues based on collected clinical data. We analyze different machine learning approaches in order to recommend an optimal model by discussing the achieved performance in terms of multiple performance metrics. Finally, we provide our recommendations and share our lessons-learned.