ARTIFICIAL INTELLIGENCE–BASED EARLY DIAGNOSIS OF CARDIOVASCULAR DISEASES: CLINICAL AND ANALYTICAL PERSPECTIVES

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for a significant proportion of global health burden. One of the major challenges in managing CVDs is delayed diagnosis, which often results in irreversible complications and increased mortality rates. In recent years, artificial intelligence (AI) technologies have emerged as promising tools for improving early diagnosis and risk stratification of cardiovascular conditions. This study aims to evaluate the effectiveness of AI-based diagnostic systems in the early detection of cardiovascular diseases compared to conventional diagnostic approaches.An observational clinical study was conducted involving 120 patients with suspected cardiovascular disorders. Participants were divided into two groups: an AI-assisted diagnostic group and a conventional diagnostic group. Electrocardiography, blood pressure measurements, and biochemical parameters were analyzed using machine learning algorithms. Statistical analysis was performed using SPSS software to compare diagnostic accuracy between the two groups. The results demonstrated that AI-assisted diagnostics significantly improved early detection rates, sensitivity, and specificity compared to traditional methods. Early-stage diagnosis was achieved in 68% of patients in the AI group versus 44% in the control group. These findings highlight the potential of AI technologies to support clinical decision-making, reduce diagnostic errors, and enhance preventive cardiology strategies.In conclusion, artificial intelligence–based diagnostic systems represent an effective and innovative approach to the early detection of cardiovascular diseases, contributing to improved patient outcomes and reduced healthcare burden

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