Coronary Artery Disease Prediction Using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation - Endang S Kresnawati; Yulia Resti; Bambang Suprihatin; M. Rendy Kurniawan; Widya Ayu Amanda (Pendidikan Matematika Universitas Muhammadiyah Bangka Belitung, 2021)
Coronary artery disease has been the leading cause of death in the world population for at least two decades (2000-2019) and has experienced the largest increase in mortality in that time span compared to other causes of death. The success of predicting coronary artery disease early based on medical data is not only beneficial for patients, but also beneficial for the stability of the country's economy. This paper discusses the prediction of coronary artery disease risk by implementing two statistical learning methods, namely Multinomial Naïve Bayes and Decision Tree with 10-fold cross validation, where numerical variables are discretized to obtain categorical variables. The results showed that the Decision Tree method has better performance than the Multinomial Naïve Bayes method in predicting coronary artery disease. The performance measure of the Decision Tree method obtained an accuracy rate of 99.63%, 100% sensitivity, 99.33% specificity, 99.23% precision, and 100% Negative Prediction Value. These measures indicate that the Decision Tree method is appropriate for predicting coronary artery disease, including independent data (other coronary artery disease data with the same predictor variables). The results of this study also show that the different references to previous studies in discretizing numerical variables can improve the performance of the method in predicting coronary artery disease.