Machine Learning Classifications of Coronary Artery Disease

TL;DR

This study applies feature selection and machine learning classifiers to diagnose coronary artery disease, finding Naive Bayes performs best with 84% accuracy on the Cleveland dataset.

Contribution

It introduces a comparative analysis of feature selection methods and classifiers for CAD diagnosis, highlighting Naive Bayes as the most effective model.

Findings

Naive Bayes achieved 84% accuracy.

Feature selection improved model performance.

Naive Bayes outperformed SVM and KNN.

Abstract

Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool, however features and algorithms must be carefully selected to get accurate classification. To this effect, three feature selection methods have been used on 13 input features from the Cleveland dataset with 297 entries, and 7 were selected. The selected features were used to train three different classifiers, which are SVM, Na\"ive Bayes and KNN using 10-fold cross-validation. The resulting models evaluated using Accuracy, Recall, Specificity and Precision. It is found that the Na\"ive Bayes classifier performs the best on this dataset and features, outperforming or matching SVM and KNN in all the four evaluation parameters used and achieving an accuracy of 84%.