Myocardial Infarction (MI), commonly known as a heart attack, occurs when blood flow to a part of the heart is blocked for a prolonged period, resulting in tissue damage. As a leading cause of death worldwide, early detection and intervention are crucial in mitigating its impact.

Our project will employ state-of-the-art machine learning techniques to analyze patient data and develop a predictive model for identifying individuals at risk of MI. By various risk factors, we aim to create a personalized risk assessment tool.

The primary objective of our model is to provide individuals with awareness of their risk status. This heightened awareness can significantly reduce diagnosis times during a cardiac event, enabling prompt initiation of treatment and minimizing the associated damages.