TY  - JOUR
AU  - Jeong, Chang-Uk
AU  - Leiby, Jacob S
AU  - Kim, Dokyoon
AU  - Choe, Eun Kyung
PY  - 2025
DA  - 2025/4/11
TI  - Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study
JO  - JMIR Aging
SP  - e64473
VL  - 8
KW  - biological age
KW  - aging clock
KW  - mortality
KW  - artificial intelligence
KW  - machine learning
KW  - record
KW  - history
KW  - health checkup
KW  - clinical relevance
KW  - gerontology
KW  - geriatric
KW  - older
KW  - elderly
KW  - aging
KW  - prediction
KW  - predictive
KW  - life expectancy
KW  - AI
AB  - Background: The global increase in life expectancy has not shown 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 aimed 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 checkups at the Seoul National University Hospital Gangnam Center as well as from the Korean Genome and Epidemiology Study. Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. Model performance was evaluated using adjusted R2 and the mean squared error (MSE) values. Shapley Additive exPlanation (SHAP) analysis was conducted to interpret the model’s predictions. Results: The Gradient Boosting model achieved the best performance with a mean (SE) MSE of 4.219 (0.14) and a mean (SE) R2 of 0.967 (0.001). SHAP analysis identified significant predictors of biological age, including kidney function markers, gender, glycated hemoglobin level, liver function markers, and anthropometric measurements. After adjusting for the chronological age, the predicted biological age showed strong associations with multiple clinical factors, such as metabolic status, body compositions, fatty liver, smoking status, and pulmonary function. Conclusions: Our aging clock model demonstrates a high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model’s applicability in routine health checkups could enhance health management and promote regular health evaluations. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2025/1/e64473
UR  - https://doi.org/10.2196/64473
DO  - 10.2196/64473
ID  - info:doi/10.2196/64473
ER  -