Why the study?
Current biological age prediction models are limited by reliance on conventional statistical methods and constrained clinical information.
Population
Koreans undergoing health checkups at Seoul National University Hospital Gangnam Center and from the Korean Genome and Epidemiology Study
Comparison
Multiple machine learning algorithms incorporating 27 clinical factors
Design
Model development and validation study
Key result
An artificial intelligence-driven Gradient Boosting model accurately predicted biological age using 27 routine clinical factors, achieving a mean squared error of 4.219 and an R2 of 0.967.
Authors
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High-accuracy AI biological age prediction from routine data is feasible; leaves open prospective outcome validation before clinical use.
Cohort (n=40,385)
Yes
Absolute Event Rate: 4.219% vs 8.244%
An AI-driven biological age prediction model using routine health checkup data demonstrated high accuracy and clinical relevance for personalized health monitoring.
Jeong et al. (2025) conducted a cohort in Healthy adults (n=40,385). Gradient Boosting biological age prediction model vs. Support Vector Machine (SVM) model was evaluated on Mean squared error (MSE) for biological age prediction. An artificial intelligence-driven Gradient Boosting model accurately predicted biological age using 27 routine clinical factors, achieving a mean squared error of 4.219 and an R2 of 0.967.
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