Classification and Detection of Coronary Heart Disease using Machine Learning

In accordance to the statistics provided by WHO, the biggest reason for death is heart diseases which account for 32% of all fatalities worldwide. Coronary heart diseases are the major contributors of all the heart diseases. It is crucial to recognize cardiovascular illnesses as soon as possible so that treatment could be given in time to save lives and reduce the fatality rate. Supervised Machine Learning is very useful in the timely detection and classification and has achieved significantly high accuracy levels. In this paper six, most coveted algorithms have been applied to the Cleveland Heart Disease data-set and the results have been compared to check which algorithm is best suited for the classification and detection of coronary heart disease. The tool used for comparative analysis is the java-based tool Weka. The simulation results exhibit that the Naive Bayes algorithm shows better accuracy for the predicting coronary diseases on the Cleveland data-set for Heart Disease.