CAD Diagnosis by Predicting Stenosis in Arteries Using Data Mining Process

Coronary artery disease (CAD) is the most common cardiovascular disease, causing death all over the world. An invasive method, angiography, is used to diagnose this disease, but it is very costly and has some side effects. Hence, noninvasive methods such as machine learning were being used for diagnosing CAD. One of the ways to detect the presence of CAD is to find out the stenotic artery. The proposed study has diagnosed whether the arteries are stenotic or not. Data preprocessing and feature selection have been done on the dataset to improve accuracy. Different supervised algorithms were applied to the selected features. The highest accuracies for left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) were obtained by random forest. Among all the arteries, LAD has the highest accuracy which means that chances of a person having LAD as stenotic are very high.