Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis

• Published in: PloS one Vol. 20; no. 3; p. e0319232
• Main Authors: Wang, Zhe, Jia, Ni
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.03.2025; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; China - epidemiology; Chronic illnesses; Cognitive ability; Computer and Information Sciences; Cross-Sectional Studies; Data collection; Data mining; Datasets; Depression - diagnosis; Depression - epidemiology; Disease; Emotions; Empirical analysis; Ethics; Female; Humans; Insomnia; Learning algorithms; Life satisfaction; Loneliness; Longitudinal studies; Machine Learning; Male; Medicine and Health Sciences; Mental depression; Mental health; Middle age; Middle Aged; Older people; People and Places; Physical Sciences; Polls & surveys; Prediction models; Psychological aspects; Questionnaires; Research and Analysis Methods; Surveys; Surveys and Questionnaires; Values; Variables; China; Shaanxi China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0319232

To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China. Participants aged ≥ 45 from the 2020 China Health and Retirement Survey (CHARLS) cross-sectional study were enrolled. Depressive mood was defined as a score of 10 or higher on the CESD-10 scale, which has a maximum score of 30. A predictive model was developed using five selected machine learning algorithms. The model was trained and validated on the 2020 database cohort and externally validated through a questionnaire survey of middle-aged and elderly individuals in Shaanxi Province, China, following the same criteria. SHapley Additive Interpretation (SHAP) was employed to assess the importance of predictive factors. The stacked ensemble model demonstrated an AUC of 0.8021 in the test set of the training cohort for predicting depressive symptoms; the corresponding AUC in the external validation cohort was 0.7448, outperforming all base models. The stacked ensemble approach serves as an effective tool for identifying depression in a large population of middle-aged and elderly individuals in China. For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.