Comparison of related studies and this work.

In response to Taiwan’s rapidly aging population and the rising demand for personalized health care, accurately assessing individual physiological aging has become an essential area of study. This research utilizes health examination data to propose a machine learning-based biological age prediction model that quantifies physiological age through residual life estimation. The model leverages LightGBM, which shows an 11.40% improvement in predictive performance (R-squared) compared to the XGBoost model. In the experiments, the use of MICE imputation for missing data significantly enhanced prediction accuracy, resulting in a 23.35% improvement in predictive performance. Kaplan-Meier (K-M) estimator survival analysis revealed that the model effectively differentiates between groups with varying health levels, underscoring the validity of biological age as a health status indicator. Additionally, the model identified the top ten biomarkers most influential in aging for both men and women, with a 69.23% overlap with Taiwan’s leading causes of death and previously identified top health-impact factors, further validating its practical relevance. Through multidimensional health recommendations based on SHAP and PCC interpretations, if the health recommendations provided by the model are implemented, 64.58% of individuals could potentially extend their life expectancy. This study provides new methodological support and data backing for precision health interventions and life extension.针对台湾地区快速老龄化的人口现状与个性化医疗需求日益增长的趋势，精准评估个体生理衰老程度已成为至关重要的研究领域。本研究利用健康体检数据，提出了一种基于机器学习的生理年龄预测模型，通过剩余寿命估计量化生理衰老水平。该模型采用LightGBM算法，相较于XGBoost模型，其预测性能（决定系数R²）提升了11.40%。实验阶段中，采用链式方程多重插补法（Multiple Imputation by Chained Equations, MICE）处理缺失数据，显著提升了预测精度，使预测性能提升了23.35%。Kaplan-Meier（K-M）估计器生存分析结果显示，该模型能够有效区分不同健康水平的人群，印证了生理年龄作为健康状态指标的有效性。此外，该模型分别识别出对男性和女性衰老影响最大的十大生物标志物，其中69.23%的标志物与台湾地区主要致死病因及已报道的健康影响核心因素重合，进一步验证了该模型的实际应用价值。基于SHAP值与皮尔逊相关系数（Pearson Correlation Coefficient, PCC）的解释结果生成多维健康建议后，若落实模型提供的健康干预方案，64.58%的受试者可实现预期寿命延长。本研究为精准健康干预与寿命延长领域提供了全新的方法学支撑与数据依据。