THE EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE EARLY DIAGNOSIS OF HEART FAILURE

Abstract

Heart failure remains a leading cause of morbidity and mortality worldwide, necessitating earlier and more precise diagnostic interventions. Traditional diagnostic methods often rely on late-stage clinical symptoms, which can delay lifesaving treatment. Recent advancements in Artificial Intelligence, specifically machine learning and deep learning architectures, have demonstrated significant potential in identifying subtle physiological changes before they manifest clinically. This article explores the effectiveness of AI algorithms in processing complex datasetsincluding electrocardiograms, electronic health records, and medical imaging to detect HF in its subclinical phases. By analyzing patterns invisible to the human eye, AI not only enhances diagnostic accuracy but also enables a shift toward proactive, personalized cardiology. The integration of these technologies into clinical practice promises to reduce hospital readmission rates and significantly improve long term patient outcomes