Machine Learning-based Model for Early Prediction of Coronary Artery Disease

Abstract

The global healthcare industry manages millions of individuals and generates enormous amounts of data. Machine learning-based algorithms are analysing complex medical information and produce superior insights. “Coronary artery disease (CAD)”, the most prevalent form of cardiac disease is getting the greatest interest in the development of predictive models due to its large number of modifiable risk factors. This research study aims at comparing five algorithms of supervised machine learning for the CAD prediction. The research utilizes the Cleveland dataset from the UCI repository for training and testing the algorithms. The results of the comparison revealed that KNN is the best algorithm with significant performance measures which can be effective in predicting CAD accurately. Therefore, it can be suggested that these predictive models, which were developed using machine learning (ML) algorithms, can help doctors identify CAD early and may lead to better results that would help to avoid adverse clinical outcomes.