Abstract
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.