International Journal of Scientific Research and Engineering Development

Heart Attack Risk Predictive Simulator is a web-based health solution for evaluating your potential risk for a heart attack that utilizes machine learning methods. The Heart Attack Risk Predictive Simulator uses machine learning techniques by being fed medical history information from a patient, including things like age and cholesterol levels (i.e., risk factors) and generates a prediction of your chances of having a heart attack in the next 12 months. The prediction is made quickly with the most accurate measurements available, and we will accomplish this with the XG Boost algorithm. The simulator will be built using a technology stack containing both Django and MySQL. The Heart Attack Risk Predictive Simulator will allow users to enter their own personal medical information, provide predictive data concerning their chance of experiencing a heart attack; recommend preventative health services based on previous data entered, and generate a predictive health (pdf) report based on the predictive data for later review. The overall goal of creating the simulator is to improve the prevention of disease through the ability to accurately predict a person's future risk of experiencing a heart attack.

P.Rajapandian, S.Kiruthisha,"Heart Attack Risk Prediction Using XG Boost Algorithm" International Journal of Scientific Research and Engineering Development, V9(3): Page(1556-1561) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.