Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults  

作　　者：Chenlin Du Zeyu Zhang Baoqin Liu Zijian Cao Nan Jiang Zongjiu Zhang 

机构地区：[1]School of Biomedical Engineering,Tsinghua University,Beijing,China [2]Tsinghua Medicine,Tsinghua University,Beijing,China [3]Institute for Hospital Management,Tsinghua University,Beijing,China [4]Department of Gynecology of Traditional Chinese Medicine,China-Japan Friendship Hospital,Beijing,China

出　　处：《Health Care Science》2024年第6期426-437,共12页科学医疗(英文)

摘　　要：Background:Frailty in older adults is linked to increased risks and lower quality of life.Pre-frailty,a condition preceding frailty,is intervenable,but its determinants and assessment are challenging.This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.Methods:The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study.Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale.We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk.A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80%of the sample and validated on a separate 20%holdout data set.Results:The study used data from 2508 community-dwelling older adults(mean age,67.24 years[range,60–96];1215[48.44%]females)to develop a pre-frailty risk assessment model.We selected 57 predictive features and built a distilled CatBoost model,which achieved the highest discrimination(AUROC:0.7560[95%CI:0.7169,0.7928])on the 20%holdout data set.The living city,BMI,and peak expiratory flow(PEF)were the three most significant contributors to pre-frailty risk.Physical and environmental factors were the top 2 impactful feature dimensions.Conclusions:An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed.Our framework incorporates a wide range of features and determinants,allowing for a comprehensive and nuanced understanding of pre-frailty risk.

关 键 词：China Health and Retirement Longitudinal Study Chinese community-dwelling older adults explainable machine learning pre-frailty risk assessment 

分 类 号：G63[文化科学—教育学]