ARTIFICIAL INTELLIGENCE–BASED PREDICTIVE MODELS FOR EARLY DETECTION OF CARDIOVASCULAR DISEASES IN YOUNG ADULTS

Abstract

Cardiovascular diseases remain the leading cause of global mortality and increasingly affect young adults due to early exposure to modifiable risk factors. Conventional cardiovascular risk assessment tools demonstrate limited sensitivity in identifying subclinical vascular dysfunction in individuals under 35 years of age. The purpose of this study was to develop and validate an artificial intelligence–based predictive model for early cardiovascular risk stratification in asymptomatic young adults. diagnosed A prospective analytical study included 328 participants aged 18–35 years without previously cardiovascular pathology. Clinical, biochemical, inflammatory, and lifestyle parameters were integrated into a gradient boosting machine learning algorithm with five-fold cross-validation. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. The developed model achieved an accuracy of 90.2%, sensitivity of 88.7%, specificity of 91.5%, and AUC of 0.95. Feature importance analysis identified LDL cholesterol, systolic blood pressure variability, body mass index, high-sensitivity C reactive protein, physical inactivity, and positive family history as dominant predictors. Notably, 23.4% of individuals classified as high-risk by the AI model had low conventional risk scores, indicating improved early detection capability. Predictive simulation modeling demonstrated a potential 18–21% reduction in projected 10-year cardiovascular event probability following AI-guided preventive intervention. The findings confirm the high discriminative capacity and clinical relevance of artificial intelligence–based predictive modeling for early cardiovascular screening in young populations.