STRATIFYHF
Published May 20, 2026 | Version v1
Conference paper Open

Differentiating Suspected and Confirmed Heart Failure Using Machine Learning and Refined Vocal Features

Authors/Creators

  • 1. Institute for Information Technologies, University of Kragujevac
  • 2. Bioengineering Research and Development Center (BioIRC), Kragujevac
  • 3. Faculty of Medicine, University of Novi Sad
  • 4. Research Centre for Discoveries in Life Sciences, Coventry University
  • 5. ROR icon University Medical Center Utrecht
  • 6. Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
  • 7. Cardiomyopathy Unit, Careggi University Hospital, Florence
  • 8. Hospital Universitario Ramón y Cajal, Madrid
  • 9. Department of Medical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac
  • 10. Department of Radiology, Faculty of Medical Sciences, University of Kragujevac

Description

Voice characteristics are an emerging, non-invasive biomarker for heart failure. This study develops a machine learning pipeline to differentiate patients with suspected heart failure from those with a confirmed diagnosis using vocal features alone, drawing on 240 patients (50 suspected, 190 confirmed) from six European medical centres who completed a multi-test voice-recording protocol. From 490 extracted voice features, collinearity filtering and LASSO regularization reduced the set to 22 key biomarkers; combined with SMOTE class-balancing, an Extra Trees classifier achieved 78.4% accuracy and a macro-F1 score of 0.76, with 89.5% sensitivity for confirmed HF, supporting refined vocal biomarkers as a reliable HF screening tool. This work was presented at the 5th Serbian International Conference on Applied Artificial Intelligence (SICAAI 2026), Kragujevac, Serbia, and was carried out within the STRATIFYHF project.

Files

11_Dasic_et_al_Differentiating_Suspected_Confirmed_HF_Vocal_SICAAI2026.pdf

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Additional details

Funding

European Commission
STRATIFYHF - Artificial intelligence-based decision support system for risk stratification and early detection of heart failure in primary and secondary care 101080905