Software environment table.

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.针对台湾地区人口快速老龄化与个性化医疗需求日益攀升的现状，精准评估个体生理衰老程度已成为至关重要的研究方向。本研究依托健康体检数据，提出了一种基于机器学习的生物年龄预测模型，该模型通过剩余寿命估计实现生理年龄的量化表征。该模型采用LightGBM算法，相较于XGBoost模型，其预测性能（以决定系数R²衡量）提升了11.40%。实验阶段，采用链式方程多重插补法（MICE）处理缺失数据后，预测精度得到显著提升，预测性能改善幅度达23.35%。采用Kaplan-Meier（K-M）估计量开展生存分析的结果显示，该模型可有效区分不同健康水平的人群，进一步证实了生物年龄作为健康状态指标的有效性。此外，该模型分别筛选出对男性与女性衰老影响最为显著的十大生物标志物，其中69.23%的标志物与台湾地区主要致死病因及此前已确认的高健康影响因子存在重合，进一步验证了该模型的实际应用价值。通过基于SHAP与皮尔逊相关系数（PCC）的可解释性分析生成多维健康建议，若落实该模型提供的健康干预方案，约64.58%的个体有望延长预期寿命。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论支撑与数据依据。