Advanced Heart Attack Risk Prediction Using a Stacked Hybrid Machine Learning Approach

This paper presents the design and implementation of a Heart Attack Prediction System using Machine learning techniques, aimed at improving early detection and risk assessment of cardiovascular diseases. Heart disease remains one of the leading causes of mortality worldwide, and timely prediction can significantly reduce fatal outcomes. The proposed system utilizes patient medical parameters such as age, blood pressure, cholesterol levels, heart rate, and other clinical attributes to predict the likelihood of a heart attack. A hybrid machine learning approach is employed, combining algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting to achieve high prediction accuracy. In addition to prediction, the system provides personalized health suggestions based on input features, making it a decision-support and preventive healthcare tool. Experimental results demonstrate improved prediction performance and actionable insights for users.