aceExplainable machine learning framework for congestive heart failure detection using GLCM-based HRV features and Bayesian network analysis
BackgroundCongestive 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).ObjectiveThis paper suggests an approach which combines Gray-Level Co-occurrence Matrix (GLCM)-based texture analysis, Bayesian-opti…
A Bayesian-optimized Quadratic Gaussian SVM using texture features of heart rate variability separated CHF from non-CHF records with 93.10% accuracy and AUC of 0.9697, while Bayesian network analysis flagged the most influential features.
Evidence
- Peer-reviewedTechnology and Health Care2026-10-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1731, v1: “Explainable machine learning framework for congestive heart failure detection using GLCM-based HRV features and Bayesian network analysis.” Truvace, 2026-10-17. /record/TRV-2026-1731 (accessed at citation time). sha256 6f5d8b6238ef95db…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1731 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.