Data Mining Techniques: New Avenues for Heart Disease Prediction

The medical management sector assembles a large volume of unexposed data on the health status of patients. At times this hidden data could be useful in diagnosing diseases and making effective decisions. For providing an appropriate way out and planning a diagnostic system based on this information, now-a-days, the newest data mining strategies are in use. In this study, a thorough review has been done on the identification of an effective heart disease prediction system (EHDPS) designed by neural network for the prediction of the risk level of cardiovascular diseases. The study focused on the observation of various medical parameters, namely, age, height, weight, BMI, sex, blood pressure, cholesterol, and obesity. Based on this study, a concept map has been designed on the prediction ways for individuals with heart disease with the help of EHDPS. The study assembled considerable information about the multilayer perceptron neural network with rear proliferation as the algorithm for data analysis. The current review work may be significant in establishing knowledge of the association between health factors related to the risk level of heart disease. The study also suggests means of early intervention and prevention of medical emergencies posed by the late detection of cardiovascular diseases, especially in the context of post COVID 19 complications.