RED RHD (Rice Early Detection for Rheumatic Heart Disease): AI-Based Adaptive Multi-Regional System for Early Detection and Murmur Classification of Rheumatic Heart Disease

This study presents RED-RHD, a machine learning methodology for early detection and classification of Rheumatic Heart Disease (RHD) using heart sound recordings. By leveraging OpenL3 deep acoustic embeddings, cloud-based workflows, and an ensemble of SVM and XGBoost classifiers, RED-RHD achieves an average precision of 95.62% for murmur detection (Normal vs. Abnormal) and 99.00% precision for systolic vs. diastolic murmur classification, demonstrating marked improvements over prior methods with poor cross-dataset generalization (e.g., specificity as low as 4.3% in ResNet-based approaches). These results confirm the systems robustness across diverse, noisy clinical datasets. Additionally, we introduce a novel dynamic adaptive model selection mechanism that enables the framework to automatically select the most appropriate pretrained machine learning model based on extracted heart sound features, optimizing prediction accuracy for different regional or demographic populations. By incorporating this adaptive intelligence, RED-RHD addresses population variability and supports precision diagnostics in globally diverse patient groups, advancing the potential for scalable, AI-driven auscultation in low-resource environments.