Delving into Heart Disease Prediction: A Comprehensive Review

The heart, as a vital organ, plays a central role in circulating blood throughout the human body. According to global health organizations, any malfunction in this crucial organ can lead to fatal outcomes, such as heart attacks, coronary heart disease, and vascular disease. Leveraging the power of deep learning and machine learning algorithms in the medical sector, this proposal aims to focus on early detection and prevention of cardiovascular disorders. The primary objective is to identify and develop an effective algorithm for the detection and prognosis of coronary artery disease. To achieve this goal, the proposal intends to utilize well-established databases pertaining to cardiovascular conditions, notably the Cleveland dataset. This comprehensive study involves a meticulous examination of existing heart disease prediction models, which have been rigorously tested for their accuracy. Among the various techniques explored, the Random Forest algorithm has demonstrated unparalleled efficacy, boasting a remarkable 100% accuracy rate. To further enhance the precision of the models, the study advocates for the careful selection of relevant features and the application of suitable preprocessing methods. By emphasizing the significance of early detection and leveraging cutting-edge algorithms, this research endeavor aims to significantly contribute to the advancement of cardiovascular healthcare, thereby potentially reducing the prevalence of cardiac diseases and improving patient outcomes.