Affiliations: University School of Information and Communication Technology, GGSIPU, Delhi, India
Correspondence:
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Corresponding author: Akansha Singh, University School of Information and Communication Technology, GGSIPU, Delhi, India. Tel.: +91 965 400 6953; E-mail: akansha.trar03@gmail.com.
Abstract: 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, non-invasive 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. This study aims to provide the best accuracy while balancing the dataset using a spreadsubsample filter. Data pre-processing and feature selection has been done on the dataset to improve accuracy. Different supervised classifiers were applied to the selected features. The highest accuracies for left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) obtained by Random Forest are 95.70%, 91.41%, and 94.38% respectively. Among all the arteries, LAD has the highest accuracy indicating that chances of a person having LAD as stenotic are very high.
