Developing a Predictive Model for Mobility Impairment in Community-Dwelling Older Adults Using Machine Learning
 
Hao-Jen Lo¹, Fang-Chun Chen¹, Chiao-Yu Huang^1,2,3 and Kuan-Liang Kuo^1,4,5*
 

Purpose: Mobility impairment among elderly is closely associated with adverse outcomes such as falls, frailty, and institutionalization. Early identification of high-risk individuals and timely intervention are crucial for delaying the onset of disability. The Taipei City Elderly Health Examination is a widely utilized preventive health service that allows individuals to understand their current health status and monitor changes over time. The aim of our study is to develop a predictive model for the risk of mobility impairment in elderly using machine learning methods.
Methods: We conducted a retrospective analysis of 2,165 community-dwelling adults aged ≥65 years who underwent health check-ups and ICOPE mobility screening at Taipei City Hospital in 2023. Thirteen features were selected via ANOVA F-value, including age, renal and hematologic markers, anthropometrics, and chronic conditions. Machine learning model training was performed using the Balanced Random Forest classifier with five-fold cross-validation.
Results: The final model achieved a mean AUC-ROC of 0.9671 and mean PR-AUC of 0.935. Precision, recall, and F1-score were 0.8549, 0.8747, and 0.8646, respectively. Mobility impairment was positively associated with age, creatinine, and waist circumference, and negatively associated with hemoglobin, and albumin.
Conclusions: The proposed model of this study shows promise in identifying older adults at risk of mobility impairment. This approach supports the integration of predictive analytics into community health to enable timely, personalized interventions that promote healthy aging.
 
(Taiwan J Fam Med 2026; 36: 21-33) DOI: 10.53106/168232812026033601003
 
Key words: healthy aging, health examination, machine learning, mobility impairment