zenodoConference paper2026-07-30
Ognjen Pavić, Lazar Dašić, Anđela Blagojević, Tijana Geroski, Nenad Filipović
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…
Lazar Dašić, Ognjen Pavić, Tijana Geroski, Anđela Blagojević, Andrej Preveden, Aleksandra Milovančev, et al.
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 su…
Teodora Jeremić, Lazar Dašić, Ognjen Pavić, Anđela Blagojević, Nenad Filipović
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…
Đorđe Ilić, Ognjen Pavić, Lazar Dašić, Anđela Blagojević, Nenad Filipović
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…
Borut Flis, Petar Vračar, Matej Pičulin, Djordje Jakovljević, Nenad Filipović, Zoran Bosnić
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…
Tijana Geroski, Mahyar Bolhassani, Narayana S. Singam, Ayman Battisha, Nenad Filipović, Dinesh Kalra, et al.
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…