Machine Learning-Based Analysis of ECG and PCG Signals for Rheumatic Heart Disease Detection in Africa: A Scoping Review (2015-2025)

This scoping review aims to comprehensively map the landscape of machine learning applications in analyzing ECG and PCG signals for Rheumatic Heart Disease detection across Africa from 2015 to 2025. As RHD disproportionately affects low and middle-income countries where gold-standard echocardiography remains inaccessible. Our purpose is to comprehensively map existing ML techniques, categorize prevalent methodologies, characterize validation approaches, and identify research gaps and implementation opportunities. Expected outcomes include: (1) a comprehensive landscape of ML approaches for RHD detection; (2) identification of highest-performing algorithms across different valvular pathologies; (3) assessment of methodological rigor in current research; and (4) recommendations for future research priorities. We anticipate this review will guide researchers, technology developers, and clinicians toward developing more accessible, validated screening tools aligned with WHO's goal to reduce RHD-related mortality by 25% by 2025.