Coronary Artery Disease Prediction using Machine Learning Algorithms

In the past decade, Cardiovascular Disease (CVD) has been one of the major death factors for humanity. Early diagnosis could increase the chances of curing patients of their disease and will help in increasing their impermanence. Contemporarily, Machine Learning algorithms are being widely used in the medical community and have been giving assuring results which has had an impact on saving lives as well as saving time. To attain better accuracy, Machine learning models are specifically trained using various datasets. Through this research, our primary intention is to obtain higher accuracies using various machine learning models that have been developed by training these datasets pertaining to heart disease prediction based on various metrics. We intend to reduce the financial cost and time by avoiding costly medical examinations. This reduces the waiting time it takes for the patients to undergo multiple such tests. In this paper, it is demonstrated that CVD can be predicted using simple medical tests, supported with advanced machine learning algorithms.