Risk Stratification and Early Diagnosis of Heart Failure
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 Risk stratification module; both are critical to improving outcomes yet remain underutilized in clinical practice. The Early Diagnosis Module attempts to identify HF before diagnostic completion, prioritizing physical examination, symptoms, blood biomarkers, and patient history while excluding post-diagnosis attributes. Multiple ML models were trained using 10-fold cross-validation, achieving promising results despite challenges posed by incomplete data. The Risk Stratification Module focuses on predicting HF risk without prior diagnoses. Using XGBoost and Random Forest models, we achieved accuracy of 0.895, sensitivity of 0.984 and an F1 score of 0.937. These results demonstrate the feasibility of integrating ML-based predictive models into clinical workflows, offering significant potential to improve early HF diagnosis and risk management. This work was presented at the 1st International Conference on AI in Medicine and Healthcare (AiMH 2025), Innsbruck, Austria, and was carried out within the STRATIFYHF project.