Yu Lisheng, Cao Shunshun, Song Botian, Hu Yangyang
Neurosurgery, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wenzhou Municipal Key Laboratory of Neurodevelopmental Pathology and Physiology, Wenzhou Medical University, Wenzhou, China.
Front Public Health. 2024 Dec 17;12:1489848. doi: 10.3389/fpubh.2024.1489848. eCollection 2024.
Frailty is an emerging global health burden, and there is no consensus on the precise prediction of frailty. We aimed to explore the association between grip strength and frailty and interpret the optimal machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to predict the risk of frailty.
Data for the study were extracted from the China Health and Retirement Longitudinal Study (CHARLS) database. Socio-demographic, medical history, anthropometric, psychological, and sleep parameters were analyzed in this study. We used the least absolute shrinkage and selection operator (LASSO) regression to filter the model for the best predictor variables and constructed six ML models for predicting frailty. The feature performance of six ML models was compared based on the area under the receiver operating characteristic curve (AUROC) and the light gradient boosting machine (LightGBM) model was selected as the best predictive frailty model. We used SHAP to interpret the LightGBM model and to reveal the decision-making process by which the model predicts frailty.
A total of 10,834 eligible participants were included in the study. Using the lowest quartile of grip strength as a reference, grip strength was negatively associated with the risk of frailty when grip strength was >29.00 kg for males or >19.00 kg for females ( < 0.001). The LightGBM model predicted frailty with optimal performance with an AUROC of 0.768 (95% CI 0.741 ~ 0.795). The SHAP summary plot showed that all features predicted frailty in order of importance, with cognitive function being considered the most important predictive feature. The poorer the cognitive function, nighttime sleep duration, body mass index (BMI), and grip strength, the higher the risk of frailty in middle-aged and older adults. The SHAP individual force plot clearly shows that the LightGBM model predicts frailty in the individual decision-making process.
The grip strength-related LightGBM prediction model based on SHAP has high accuracy and robustness in predicting the risk of frailty. Increasing grip strength, cognitive function, nighttime sleep duration, and BMI reduce the risk of frailty and may provide strategies for individualized management of frailty.
衰弱是一个新出现的全球健康负担，目前对于衰弱的精确预测尚无共识。我们旨在探讨握力与衰弱之间的关联，并使用夏普利值附加解释法（SHAP）来解释最优机器学习（ML）模型，以预测衰弱风险。
本研究数据取自中国健康与养老追踪调查（CHARLS）数据库。本研究分析了社会人口统计学、病史、人体测量学、心理和睡眠参数。我们使用最小绝对收缩和选择算子（LASSO）回归来筛选模型，以找出最佳预测变量，并构建了六个用于预测衰弱的ML模型。基于受试者工作特征曲线下面积（AUROC）比较了六个ML模型的特征性能，并选择了轻梯度提升机（LightGBM）模型作为最佳衰弱预测模型。我们使用SHAP来解释LightGBM模型，并揭示该模型预测衰弱的决策过程。
本研究共纳入10834名符合条件的参与者。以握力最低四分位数为参照，当男性握力>29.00千克或女性握力>19.00千克时，握力与衰弱风险呈负相关（<0.001）。LightGBM模型预测衰弱的性能最佳，AUROC为0.768（95%CI 0.741~0.795）。SHAP汇总图显示，所有特征均按重要性顺序预测衰弱，其中认知功能被认为是最重要的预测特征。中年及老年人的认知功能、夜间睡眠时间、体重指数（BMI）和握力越差，衰弱风险越高。SHAP个体作用力图清楚地表明，LightGBM模型在个体决策过程中预测衰弱。
基于SHAP的与握力相关的LightGBM预测模型在预测衰弱风险方面具有较高的准确性和稳健性。增加握力、认知功能、夜间睡眠时间和BMI可降低衰弱风险，并可能为衰弱的个体化管理提供策略。
