Key result
Explainable machine learning using digital auscultation achieves 85% accuracy for detecting and classifying VHD.
Why the study?
Traditional cardiac auscultation for valvular heart disease is subjective and clinician-driven, prompting the need for explainable machine learning frameworks using digital auscultation data.
Does an explainable ML framework using digital acoustic features accurately detect and classify valvular heart diseases?
Observational
Does an explainable ML framework using digital acoustic features accurately detect and classify valvular heart diseases?
An explainable machine learning framework using digital auscultation data achieved 85% accuracy in classifying valvular heart diseases, highlighting its potential for objective, automated screening.
ML-based digital auscultation may aid VHD detection; leaves open prospective clinical validation before practice change.
Valvular heart diseases (VHDs), including mitral regurgitation (MR), aortic stenosis (AS), mitral stenosis (MS), and mitral valve prolapse (MVP) represent a significant global health burden particularly among older adults. Digital auscultation platforms can transform traditional cardiac assessments that are primarily subjective and clinician-driven into a data-driven diagnostic tool enabling advanced signal processing, feature extraction and machine learning (ML)-based interpretation. This creates new opportunities for early, objective, and precise disease screening, diagnosis, longitudinal monitoring, and personalized clinical decision-making. In this study, we present an explainable ML framework for early detection and precise classification of VHDs using digital auscultation data. Acoustic features extracted from digital auscultation data are used to build ML models on a public dataset followed by validation using clinical hospital data. Shapley Additive Explanations (SHAP) helps create more understandable models for early detection by pinpointing unique acoustic characteristics of VHD, which enhances the interpretability and accuracy of ML models. The SHAP tree explainer is utilized to improve interpretability and guide feature selection by identifying unique, consistent, and overlapping features relevant to VHDs, providing physiological insights and enhancing model transparency. Among the five models assessed, XGBoost with SHAP stood out as the most reliable, delivering high interpretability and 85 % accuracy on the clinical dataset, achieving condition-specific accuracies with minimal variability across different practitioners. By combining predictive performance with explainability, the proposed framework shows high promise in objective screening, early diagnosis, and informed clinical decision-making for VHDs.
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V et al. (2026) conducted an observational in Valvular heart diseases (VHDs). Explainable ML framework (XGBoost with SHAP) using digital auscultation data vs. Other ML models was evaluated on Accuracy on the clinical dataset. An explainable machine learning framework using XGBoost with SHAP and digital auscultation data achieved 85% accuracy for detecting and classifying valvular heart diseases.
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