Objective To develop a predictive model for disability risk in older adults using machine learning algorithms. Methods A convenience sample of 13,809 older adults (aged ≥60 years) was recruited from seven medical institutions, three communities, and five nursing homes in Zunyi City, Guizhou Province. Participants were randomly divided into a training set (n = 9667) and a validation set (n = 4142) at a 7:3 ratio. Disability status was used as the outcome variable. Nine machine learning algorithms—logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine, artificial neural network, K‑nearest neighbor, and naïve Bayes—were used to construct prediction models. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, and other metrics, and the best‑performing model was selected. The SHapley Additive exPlanations (SHAP) method was used for interpretability analysis of the optimal model. Results Among the 13,809 participants, 5308 (38.44%) were identified as having disability. Among the nine models, LightGBM achieved the highest AUC (0.859), accuracy (0.792), precision (0.771), sensitivity (0.651), specificity (0.880), and F1 score (0.706). Conclusion Among the developed prediction models, the LightGBM‑based model demonstrated superior overall predictive performance in internal validation, providing a reference for disability management in older adults.
Shaoting Yang, Heting Liang, Yamin Peng et al.· Clinical Interventions in Ag...· 0 citations
In large-scale epidemiological studies, it is often of interest to investigate joint relationships between longitudinal and time-to-event outcomes with exposures that are trajectories or functions. Our motivation study is the Objective Physical Activity and Cardiovascular Health (OPACH) Study, which collected accelerometry-measured physical activity in 6,489 older women. One of the scientific aims is to understand sedentary behavior accumulation patterns and its association with physical function (longitudinal) and mortality (time-to-event). We propose a novel approach that first converts raw accelerometry data into daily sitting bout accumulation profiles, which are treated as functional covariates, and then develop a functional joint model for longitudinal and time-to-event outcomes that incorporates a baseline functional covariate. The longitudinal process is modeled using functional data methods and linked to the survival process through functional principal component scores. Both sub-models include interpretable linear scalar-on-function regression coefficients to capture flexible dose-response associations between sitting accumulated across varying bout durations and health outcomes. Estimation is carried out via an efficient expectation-maximization (EM) algorithm with penalized spline approximations. Simulation studies demonstrate accurate parameter estimation and reliable model selection. Application to the OPACH data reveals flexible and interpretable dose-response relationships between sitting bout durations, physical function, and mortality.
Luo Xiao, Wenyi Wang, Yumeng Zhang et al.· 0 citations