Differentiating Suspected and Confirmed Heart Failure Using Machine Learning and Refined Vocal Features
Lazar Dašić, Ognjen Pavić, Tijana Geroski, Anđela Blagojević, Andrej Preveden, Aleksandra Milovančev, Lazar Velicki, Nduka Okwose, Anne Nelissen, Renae J. Stefanetti, Sarah Charman, Alessandra Fornaro, Marta Jimenez-Blanco Bravo, Nebojša Zdravković, Snežana Lukić, Đorđe Jakovljević, Nenad Filipović
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.