Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly...

Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depression risks among disabled elderly individuals.

The data for this study was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS). In this study, disability was defined as a functional impairment in at least one activity of daily living (ADL) or instrumental activity of daily living (IADL). Depressive symptoms were assessed by using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D10). We employed SPSS 27.0 to select independent risk factor variables associated with depression among disabled elderly individuals. Subsequently, a predictive model for depression in this population was constructed using R 4.3.0. The model's discrimination, calibration, and clinical net benefits were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curves.

In this study, 3,107 elderly individuals aged 60 years and older with disabilities were included. Poor self-rated health, pain, absence of caregivers, cognitive impairment, and shorter sleep duration were identified as independent risk factors for depression in disabled elderly individuals. The XGBoost model demonstrated superior performance in the training set, while the logistic regression model outperformed it in the validation set, with AUCs of 0.76 and 0.73, respectively. The calibration curve and Brier score (Brier: 0.20) indicated a good model fit. Moreover, decision curve analysis confirmed the clinical utility of the model.

The predictive model exhibits outstanding predictive efficacy, greatly assisting healthcare professionals and family members in evaluating depression risks among disabled elderly individuals. Consequently, it enables the early identification of elderly individuals at high risk for depression.