Intelligent Machine Learning Framework for Early Heart Disease Prediction and Risk Assessment

Abstract

Heart disease continues to be a leading cause of mortality worldwide, emphasizing the need for accurate and early diagnosis. This project presents an intelligent machine learning framework designed for early prediction and risk assessment of heart disease using clinical and patient health data. The proposed system analyzes key attributes such as age, blood pressure, cholesterol levels, blood sugar, chest pain type, and electrocardiogram results to identify underlying patterns associated with cardiovascular conditions. Multiple supervised learning algorithms, including Logistic Regression, Random Forest, and Support Vector Machines, are implemented and evaluated to determine the most effective predictive model. The system incorporates data preprocessing, feature selection, and model optimization techniques to enhance prediction accuracy and reliability. Experimental results demonstrate that the proposed approach significantly improves diagnostic performance compared to traditional methods, providing fast and data-driven decision support. This AI-based solution has the potential to assist healthcare professionals in early detection, reduce diagnostic errors, and improve patient outcomes through timely medical intervention