Predicting fall risk among older adults with sarcopenia in China using machine learning models: a six-year longitudinal study from CHARLS

Falls constitute potentially devastating health events for older adults with sarcopenia, whereas there remains a critical gap in validated fall risk prediction models tailored to this vulnerable population in China. This study aims to develop machine learning algorithms for predicting 6-year fall risk among patients with sarcopenia. Data were used from the China Health and Retirement Longitudinal Study (CHARLS) spanning from 2013 to 2018. A total of 110 input variables at the baseline level were regarded as candidate features. Sarcopenia cases were identified according to the Asian Working Group for Sarcopenia 2019 criteria. Six machine learning models were developed through rigorous cross-validation, with model performance evaluated using accuracy, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve (AUC), to estimate the 6-year fall risk prediction models for patients with sarcopenia. Among 1,087 participants with sarcopenia (mean age 71 years, 68.54% female), 246 experienced falls during follow-up. The random forest model demonstrated superior predictive performance among the six models, achieving an AUC of 0.971, sensitivity of 89.31%, specificity of 95.19%, and accuracy of 92.26%. Feature importance analysis identified 48 key predictors, with functional capacity measures, psychosocial factors, and cognitive function emerging as the strongest risk determinants. The optimized random forest algorithm provides an effective tool for identifying high-risk sarcopenia patients who may benefit from targeted fall prevention strategies. These findings underscore the importance of multidimensional interventions addressing functional decline, cognitive impairment, and psychosocial well-being in sarcopenia management.