Prediction Model for Diagnosing Heart Disease Using Classification Algorithm

Abstract

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.