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

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 scal...