ArticleFrontiers in public health2024
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Identifying depression in middle-aged and older adults stratified by chewing ability using machine learning models: a nationwide cross-sectional study in China.BMC public health · 2026Article
- Unveiling the Digital Phenotype of Physical Activity Behavior in Community-Dwelling Older Adults Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- AI in geriatric psychiatry: precision meets human experience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Machine Learning-Based Depression Risk Prediction Models for Older Adults Analyzing From the Perspective of the Health Ecology Model: A Scoping Review.Neuropsychiatric disease and treatment · 2026Review
- Prediction of depressive symptoms in middle-aged and older adult hospitalized patients with chronic diseases: a multicenter study in Anhui, China using machine learning methods.Frontiers in public health · 2026Article
- Cross-national predictive correlates of depressive symptoms among middle-aged and older adults with chronic conditions: an explainable machine learning analysis.Frontiers in psychiatry · 2026Article
- Detection of depression risk among older adults using home-deployed socially assistive robots: a real-world study.Frontiers in psychiatry · 2026Article
- Explaining factors influencing students' depression with a deep learning approach.Frontiers in psychology · 2025Article
- Identifying risk factors for depression and positive/negative mood changes in college students using machine learning.Frontiers in public health · 2025Article
- Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health Survey.Frontiers in public health · 2025Article
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4 authors.
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Abstract
Objective: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression. Methods: This research selected 7,880 older adult people by utilizing data from the 2020 China Health and Retirement Longitudinal Study. Thereafter, the dataset was classified into training and testing sets at a 6:4 ratio. Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the older adult. To compare the differences in the ROC curves of the different models, the Delong test was conducted. Meanwhile, to evaluate the models' performance, this research performed decision curve analysis (DCA). Thereafter, the Shapely Additive exPlanations values were utilized for model interpretation on the bases of the prediction results' substantial contributions. Results: The range of the area under the curve (AUC) of each model's ROC curves was 0.648-0.738, with significant differences ( Conclusion: The occurrence of depression among China's older adult population and the critical factors leading to depression can be predicted and identified, respectively, by ML algorithms.
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