Published Paper Details:

Cardiovascular diseases (CVDs), particularly myocardial infarctions (heart attacks), remain one of the leading causes of mortality worldwide. A primary contributing factor to this high mortality rate is delayed diagnosis and inadequate implementation of preventive strategies. In response, this study presents a machine learning-based predictive framework for assessing the risk of heart attacks using a combination of patient health indicators and lifestyle factors. The dataset was subjected to comprehensive preprocessing, including label encoding of categorical attributes and normalization of numerical features. A comparative analysis was conducted among several classification algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, and Artificial Neural Networks. This work illustrates the potential of artificial intelligence to support early detection, enhance preventive healthcare interventions, and contribute to reducing the global burden of cardiovascular diseases.