Coronary Artery Disease Prediction Techniques: A Survey

Abstract

Machine learning has become a salient part of our life nowadays. It has a significant effect on the medical decision support system also. In the healthcare domain, it is beneficial to predict the disease and perform analysis to derive useful patterns from the electronic health records to reduce the toll. The primary cause of death worldwide is coronary artery disease (CAD), also known as atherosclerosis. It occurs when any of the arteries get blocked, resulting in weak or no blood flow to parts of a heart, leading to a heart attack. The prediction of CAD at an early stage is possible with the help of machine learning techniques like support vector machine, artificial neural network, k-nearest neighbors, decision trees, logistic regression, fuzzy rule-based methods, and many more. This paper gives insights into the research done on the prediction of this disease. We reviewed in-depth knowledge of the disease, various diagnostic techniques, and available datasets. Finally, we discussed and concluded how the machine learning technique creates an impact on predicting this disease.