Abstract
Heart Disease is one of the most prevalent diseases that can lead to reduce the life span of human beings now a days. The initial finding of abnormal heart conditions is important to detect heart problems and avoid sudden cardiac death. Heart Disease are also known as Cardiovascular Disease which happen due to unhealthy lifestyle, smoking, alcohol and high intake of fats which may cause hypertension, high blood pressure, diabetes. They are caused by disorders of heart and blood vessels, and includes coronary heart disease (heart attacks). Data mining plays a major role in the construction of an intellectual prediction model for healthcare system to detect Heart Disease (HD) using patient data sets, which support doctors in diminishing mortality rate due to heart attack. In this review, we focus the novel and unique aspects of cardiovascular disease health and the methodologies used to predict the CVD.
KEYWORDS: Data mining, Classification, Particle Swarm Optimization, Cardiovascular Disease(CVD).