ARTIFICIAL INTELLIGENCE-BASED EARLY DETECTION OF CARDIOVASCULAR DISEASES AND PREDICTION OF INDIVIDUAL CARDIOVASCULAR RISK

Abstract

Cardiovascular diseases remain among the leading causes of mortality and disability worldwide, representing one of the most significant challenges for modern healthcare systems. The increasing prevalence of arterial hypertension, coronary artery disease, chronic heart failure, and cardiac arrhythmias necessitates the development of more accurate and efficient diagnostic approaches. In recent years, the rapid advancement of artificial intelligence technologies has initiated a new stage in cardiovascular medicine by improving data analysis, diagnostic precision, and individualized risk assessment. This article analyzes the scientific potential of machine learning and deep learning algorithms in the early detection of cardiovascular diseases through the integration of electrocardiography, echocardiography, computed tomography, and clinical biomarkers. The findings demonstrate that artificial intelligence-based systems exhibit higher diagnostic sensitivity and specificity compared with conventional statistical models. Furthermore, the integration of multimodal clinical data significantly improves the prediction of individual cardiovascular risk and enhances preventive medical strategies. Nevertheless, algorithmic bias, insufficient clinical validation, ethical concerns, and data security issues continue to limit the widespread implementation of artificial intelligence technologies in routine clinical practice.