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.
Heart failure and chronic obstructive pulmonary disease often present with overlapping clinical signs, making differential diagnosis challenging. This work applies machine learning, primarily random forest ensembles trained on heterogeneous clinical data (physical examination, bl…
Heart failure presents with a wide range of symptoms that affect patients' quality of life. This study uses physical-examination data and blood biomarkers to predict the emergence of thirteen individual HF-related symptoms (e.g. dyspnea, orthopnea, peripheral oedema, pulmonary cr…
Heart failure is one of the most life-threatening diseases of the modern era, with high global mortality and morbidity rates, motivating the need for long-term outcome prediction. One established tool is the MAGGIC Risk Calculator for Heart Failure, which predicts 3-5 year mortal…
Morbidity and disease severity in heart failure are commonly assessed using New York Heart Association (NYHA) classes. This study uses non-invasive data — physical examination, symptoms and disease history — to classify patients into four morbidity classes approximati…
Distinguishing heart failure with reduced ejection fraction (HFrEF) from heart failure with preserved ejection fraction (HFpEF) is clinically important but challenging. This paper presents an end-to-end deep learning pipeline for automated three-class classification (healthy, HFr…
Heart failure (HF) affects over 64.3 million people worldwide. As a part of the StratifyHF project, we developed a decision support system (DSS) to enhance HF prediction and diagnosis through machine learning (ML) approaches. The DSS comprises two modules: Early diagnosis and Ris…