Implementasi Bagging Naive Bayes untuk Klasifikasi Stenosis Left Anterior Descending (LAD), Left Circumflex Artery (LCX) dan Right Coronary Artery (RCA) dalam Diagnosis Coronary Artery Disease (CAD) - Amalia Luhung; Muhammad Tanzil Furqon; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Coronary Artery Disease (CAD) is a disease that occurs due to the accumulation of atherosclerotic plaque that causes blockage (stenotic) in the tunica intima lining of coronary arteries. The coronary arteries are Left Anterior Descending (LAD), Left Circumflex Artery (LCX), and Right Coronary Artery (RCA). Stenosis in coronary arteries can cause heart attacks and even death. Diagnosis needs to be done quickly to reduce the impact of CAD so, a system was built to help find out LAD, LCX, and RCA stenosis through classification. Classification is done to classify patients' coronary arteries into normal or stenotic classes using the Bagging Naive Bayes method. This method allows the classification to be carried out by several predictor models made based on bootstrap by sampling with replacement to get aggregate results. The steps taken to implement this method are preprocessing, bootstraping, Naive Bayes classification, voting. The highest accuracy in the LAD classification obtained was 0.7573 when the classification was done using 200 data, 25 bootstrap samples (T), and the classification was carried out with all features. Its result ​​of precision, sensitivity and specificity are 0.8065, 0.7938, and 0.7012. In LCX classification the highest accuracy achieved is 0.7282 when the classification is done using 200 data, T = 1, and the classification is done with the features selected. Precision, sensitivity, and specificity result are 0.9042, 0.7262, and 0.7368. Whereas in the RCA classification the highest accuracy achieved is 0.7282 when the classification was carried out using 150 data, T = 1, and the classification was carried out with the results of the selection of precision, sensitivity and specificity values ​​0.9242, 0.7262, and 0.7368. The intended feature selection method is Pearson's chi-squared and One-way ANOVA.