Development of an AI-Driven Biological Age Prediction Model Using Comprehensive Health Check-Up Data (Preprint)

BACKGROUND The global increase in life expectancy has not been paralleled by a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information. OBJECTIVE This study aims to develop and validate an aging clock model using artificial intelligence, based on comprehensive health check-up data, to predict biological age and assess its clinical relevance. METHODS We used data from Koreans who underwent health check-ups at Seoul National University Hospital Gangnam Center and the Korean Genome and Epidemiology Study (KoGES) HEXA data. Our model incorporated 39 clinical factors and employed machine learning algorithms, including linear regression, Elastic Net, LASSO, Ridge regression, Random Forest, Support Vector Machine (SVM), Gradient Boost, and K-nearest Neighbors. Model performance was evaluated using adjusted R-squared and Mean Squared Error (MSE). SHAP analysis was conducted to interpret the model's predictions. RESULTS The SVM model achieved the best performance with an MSE of 9.38 and an R2 of 0.927. SHAP analysis identified significant predictors of biological age, including markers of kidney function, metabolic health, and anthropometric measurements. The predicted biological age showed strong associations with multiple clinical factors, such as pulmonary function, atrophic gastritis, intestinal metaplasia and body compositions. CONCLUSIONS Our aging clock model demonstrates high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model's applicability in routine health check-ups could enhance health management and promote regular health evaluations. CLINICALTRIAL N/A