Deep learning models on Heart Disease Estimation - A review

Abstract

Heart disease, also known as cardiovascular disease (CVD), is the foremost among all widespread diseases in the people community. Any disorder that affects the heart's function is typically called heart disease. Narrowing or blockage of the coronary arteries, which supply blood to the heart, is the most common cause of heart failure. Coronary Artery Disease (CAD) is a common form of heart disease and the leading cause of heart attacks. Nowadays, there is no age limit for people to get affected by this disease. There are so many diagnosis methods available where most are costly, the risk involved, and technical experts are needed to perform the disease diagnosis. Clinical research has pointed out different factors that increase the risk of CAD and heart attack. These factors can be categorized into two types, i.e., risk factors that cannot be changed and those that can be changed. Sex, age and family history are those factors that cannot be altered. In contrast, factors related to a subject's lifestyle, e.g., smoking, high cholesterol, high blood pressure and physical inactivity, can be changed. This paper reviews various deep learning techniques involving heart disease prognostic and their accuracy in predicting that they can be treated in advance to prevent fatalities.