Key result
Novel ML model using HRV texture features detects congestive heart failure with ~93% accuracy.
Why the study?
Conventional machine learning models for CHF detection operate as black boxes, limiting interpretable insights into HRV physiological mechanisms.
Does an explainable machine learning framework using GLCM-based HRV features accurately detect congestive heart failure?
Does an explainable machine learning framework using GLCM-based HRV features accurately detect congestive heart failure?
A novel machine learning architecture combining GLCM-based HRV texture analysis and Bayesian optimization achieved high diagnostic accuracy (93.10%) for detecting congestive heart failure with enhanced interpretability.
Supports interpretable ML for automated CHF detection from HRV; leaves open prospective clinical validation before practice adoption.
Background Congestive heart failure (CHF) is one of the main causes of cardiovascular morbidity and mortality. While conventional machine learning (ML) models demonstrate acceptable classification results, their operation is typically based on a black-box approach which does not allow to obtain an interpretable insight into the physiological mechanism of heart rate variability (HRV). Objective This paper suggests an approach which combines Gray-Level Co-occurrence Matrix (GLCM)-based texture analysis, Bayesian-optimized ML and Bayesian network inference to increase the accuracy and clinical interpretability of CHF prediction and provide a deeper understanding of physiological mechanisms. Methods Two-dimensional representation of HRV data taken from the PhysioNet database was used for extracting texture-based biomarkers using GLCM. Most discriminatory biomarkers were selected by the Kruskal-Wallis test and ML models were optimized by Bayesian approach. Performance of models was estimated by traditional classification metrics and Bayesian network analysis with posterior probabilities and sensitivity analysis of nonlinear dependencies between HRV texture-based features was performed. Results Best performance was achieved by Bayesian-optimized Quadratic Gaussian SVM, with 93.10% accuracy, 90.91% sensitivity, 94.44% specificity and AUC equal to 0.9697. The Bayesian network analysis revealed Dissimilarity, Cluster Prominence and Cluster Shade as the most important HRV texture biomarkers related to CHF. Conclusion The novel XAI architecture is based on Bayesian optimization of ML along with Bayesian probability theory, which helps deliver precise and clinically relevant CHF diagnosis for early disease detection and personalized risk stratification.
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Habibullah et al. (2026) studied Congestive heart failure (CHF). Bayesian-optimized machine learning with GLCM-based HRV texture features was evaluated on Congestive heart failure detection accuracy. A Bayesian-optimized Quadratic Gaussian SVM using GLCM-based HRV texture features detected congestive heart failure with 93.10% accuracy, 90.91% sensitivity, and 94.44% specificity.
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