EARLY DETECTION AND PROGNOSIS OF CHRONIC HEART DISEASES USING ARTIFICIAL INTELLIGENCE

• Rashidova Zahro

  Tashkent State Medical University No. 1 of the General Medicine Faculty, Student Group 109 Tashkent, Uzbekistan

  Author

• Fazliddin Arzikulov

  Assistant of the Department of Biomedical Engineering, Informatics and Biophysics at the Tashkent State Medical University

  Author

Cardiovascular diseases remain one of the leading causes of mortality and disability worldwide. The high prevalence of these diseases, their complex pathophysiology, and multifactorial etiology necessitate the introduction of new approaches for early detection, accurate diagnosis, and effective prognosis. Early identification of high-risk cardiovascular conditions can reduce the likelihood of acute myocardial infarction and other life-threatening complications, significantly lowering mortality rates among patients.Traditional clinical and statistical analysis methods have limited capacity to fully capture the multidimensional and highly complex data required for cardiovascular risk assessment, prediction of cardiac events, comprehensive analysis of medical imaging, development of individualized treatment strategies, and forecasting disease progression. These limitations are largely explained by the complex interactions among genetic, metabolic, hemodynamic, and environmental factors. Artificial intelligence (AI) has emerged as an advanced computational technology capable of deeply analyzing large volumes of medical data, identifying hidden patterns, and generating accurate prognostic predictions, thereby playing a crucial role in modern cardiology. AI-based models demonstrate high effectiveness in the early diagnosis, risk stratification, and optimization of individualized treatment plans for heart failure, atrial fibrillation, valvular heart diseases, hypertrophic cardiomyopathy, congenital heart defects, and other cardiovascular pathologies.In clinical practice, artificial intelligence enhances the accuracy of cardiovascular disease detection, supports diagnostic and decision-making processes, classifies patients according to risk levels, and predicts disease outcomes. Modern AI algorithms are designed to identify subtle and hidden relationships within large-scale, complex healthcare datasets, offering greater potential for solving complex clinical problems compared to traditional approaches.

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