An Intelligent AI-Driven Framework for Early Prediction of Heart Disease Using Advanced Machine Learning Techniques

Early prediction of heart disease is critical for reducing mortality and improving patient care. Heart disease is one of the leading causes of death worldwide, and timely diagnosis can save lives. Traditional diagnostic methods are time-consuming and sometimes fail to detect early-stage risk. This paper proposes an intelligent AI-driven framework for the early prediction of heart disease using advanced machine learning techniques. The framework incorporates data preprocessing, feature selection, and multiple classification algorithms including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN). The proposed system is evaluated on a publicly available dataset, considering multiple patient attributes such as age, blood pressure, cholesterol, diabetes, and lifestyle factors. Performance metrics such as accuracy, precision, recall, and F1-score are computed to assess model performance. Comparative analysis demonstrates that the proposed framework outperforms traditional diagnostic approaches and provides a reliable, efficient, and automated method for early detection. The research aims to assist healthcare professionals in making informed decisions, ultimately enhancing patient outcomes.