Coronary Artery Disease Prediction Model using CART and SVM: A Comparative Study

Abstract

Heart disease is the major cause of mortality worldwide. Clinical Decision Support System is developed to measure risk level of heart disease and detect heart disease using machine learning methods. Many cases showed that heart disease may not be detected until the person encounters indications of a heart disease. Hence, the research goal is to construct and compare coronary artery disease prediction model using CART and SVM. The model identifies whether the patient has coronary artery disease or not. The result shows that CART and SVM has the same performance of accuracy of 88,33%. For sensitivity, CART has slightly better performance than SVM. While for specificity, SVM has better performance than CART.