THE ROLE OF ARTIFICIAL INTELLIGENCE IN EARLY DETECTION OF CARDIOVASCULAR DISEASES

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, often progressing silently until the onset of severe complications. Early detection is critical in mitigating these outcomes and improving patient survival through timely intervention and personalized care. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML), deep learning (DL), and natural language processing (NLP), have revolutionized diagnostic approaches in cardiology by enabling the rapid analysis of large-scale clinical data and imaging modalities.This study explores the integration of AI technologies into early CVD detection workflows. Predictive models such as Random Forests, Support Vector Machines, and Convolutional Neural Networks were employed to process multidimensional datasets including patient demographics, electrocardiogram (ECG) signals, echocardiograms, and laboratory biomarkers. Real-time monitoring systems and NLP algorithms further enhanced the extraction of clinically relevant information from unstructured data, facilitating early diagnosis and risk stratification.The results demonstrate that AI-driven models consistently outperform traditional diagnostic methods, achieving higher sensitivity, specificity, and predictive accuracy. In particular, deep learning algorithms excelled in ECG and imaging interpretation, while ML-based predictive tools offered reliable risk classification across diverse patient profiles. Visual analyses confirmed robust performance through ROC and precision-recall assessments, indicating high clinical reliability.In conclusion, the findings underscore the transformative potential of AI in the early detection of cardiovascular diseases. By improving diagnostic accuracy, enabling personalized risk assessments, and supporting remote monitoring, AI paves the way for a more proactive and data-driven approach to cardiovascular healthcare. Addressing challenges related to data quality, ethical considerations, and system integration will be essential for the sustainable and equitable implementation of AI across global health systems.