Predictive Machine Learning Methodology Framework for Heart Disease Detection

Abstract

Heart disease remains the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. The complexity and multifactorial nature of heart disease necessitate innovative approaches for early detection and accurate risk assessment. Nowadays healthcare systems produce extensive patient data, encompassing electronic health records, clinical measurements, imaging studies, and laboratory results, thereby generating substantial datasets suitable for predictive modeling. Machine learning is one of the predictive modeling approaches that easily detect the heart disease based on the symptoms. This article details comprehensive predictive machine learning methodology framework designed for highly accurate heart disease detection. The conceptual machine learning framework systematically addresses the challenges inherent in cardiovascular risk prediction through a multi-stage approach encompassing data preprocessing, feature selection optimization, model development, and rigorous validation protocols. This study presents a comprehensive predictive machine learning methodology framework specifically designed for heart disease detection, integrating advanced dataset analysis, advanced data preprocessing, feature engineering, data transformation, data partitioning, classification, and performance evaluation steps of the model-building approach.