A Comparative Study of Heart Disease Prediction Using Classification Techniques

In today's world, the most challenging thing for people is to sustain good health. One of the most significant impacts on people's health and lives is heart disease (HD). Heart failure is the number one cause of the greatest number of deaths worldwide. The goal of this paper is to evaluate classification techniques to see which one is the most accurate in predicting HD using R software. Statistical analysis helps in mining and examining the important factors of HD and can aid in determining whether or not a patient has a cardiac condition. In this paper, the potential of six classification techniques is used to predict heart failure. Namely, Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM). According to the results of the analysis, KNN outperforms the other classification techniques in HD diagnosis.