Assessment of Morbidity in Patients with Heart Failure Using Traditional Machine Learning Techniques

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 approximating NYHA grading, comparing random forest, decision tree and support vector machine classifiers. The random forest model performed best, achieving 79.2% classification accuracy, compared with 46% and 44% for the decision tree and SVM respectively. 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.