Analysis of Cardiovascular Diseases with Causes and Future

Authors- Vishav Mehra

Abstract--A complicated clinical disease known as heart failure manifests as a variety of symptoms and indicators. Even with improvements in our knowledge of its pathogenesis and the availability of several treatment options, it continues to be a major source of morbidity and death. Precise risk assessment is necessary to direct managerial choices and support patient-focused treatment. While recognized risk variables have historically been the focus of epidemiological studies to predict mortality in heart failure, newer machine learning algorithms provide a unique method for uncovering other important predictors. In this review, we examine the role of supervised learning algorithms in predicting mortality in heart failure, with a view to facilitating the development of evidence-based treatment guidelines and health care policies.

DOI: /10.61463/ijset.vol.13.issue2.433