(untitled) (International journal of medical sciences, 2026)
Cardiovascular diseases are among the leading causes of morbidity and mortality worldwide, making early diagnosis a critical factor in reducing complications and improving patient outcomes. This article explores artificial intelligence-based algorithms for the early detection of heart diseases. Machine learning and deep learning techniques are analyzed using electrocardiogram (ECG) signals, clinical indicators, and laboratory data. The findings indicate that artificial intelligence significantly enhances diagnostic accuracy and reliability while supporting clinical decision-making. The proposed algorithmic approach enables timely diagnosis, risk stratification, and the development of personalized treatment strategies, thereby improving the overall quality of healthcare services.