The STRATIFYHF project develops an AI-based decision support system integrating patient data with computational modelling to support risk stratification, diagnosis and progression prediction in heart failure. This study evaluates the cost-effectiveness of AI-enhanced ECG screening for asymptomatic left ventricular dysfunction in Serbia using decision-tree and Markov modelling. Screening at age 65 was estimated to cost around \$23,351 per quality-adjusted life year (QALY) gained, below the common \$30,000 willingness-to-pay threshold, with comparable results at ages 55 and 75. The analysis highlights that cost-effectiveness is highly sensitive to test performance and screening costs, motivating further validation of the AI algorithm. This work was presented at the 4th Serbian International Conference on Applied Artificial Intelligence (SICAAI 2025), Zlatibor, Serbia, and was carried out within the STRATIFYHF project.
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…
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…
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 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…
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…