Artificial Intelligence in Early Diagnosis of Cardiovascular Diseases: A Clinical Data-Based Study

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, underscoring the urgent need for accurate and timely diagnosis. Traditional diagnostic methods, while effective, often face limitations in detecting early-stage abnormalities due to human error, subjective interpretation, and variability in clinical data. This study investigates the application of Artificial Intelligence (AI) techniques—specifically machine learning (ML) and deep learning (DL)—to analyze clinical datasets for the early diagnosis of CVDs. By leveraging large-scale patient data, including demographic information, medical history, laboratory results, and imaging features, AI models were trained and validated to identify high-risk patients. Comparative evaluation of algorithms such as logistic regression, support vector machines, random forests, and convolutional neural networks demonstrated superior predictive performance by deep learning architectures, with accuracy exceeding 90% in early detection tasks. The results highlight AI’s potential in reducing diagnostic delays, enhancing risk stratification, and supporting clinical decision-making. Furthermore, the integration of explainable AI approaches provides interpretability, ensuring trust and adoption in medical practice. This study concludes that AI-driven diagnostic systems, when combined with clinical expertise, can revolutionize early detection of cardiovascular diseases, thereby reducing disease burden and improving patient outcomes.