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A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study
Jing Xu, Wenjin Zhang, Wenli Liu. Scientific Reports, 2025-07-24
Topic: Risk, prevention and care in population studies
This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions.Data from the 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS) were used, including 1,921 elderly individuals. Depression was assessed with the CESD-10 scale. Three machine learning models-Gradient Boosting, Random Forest, and Boosted XGBoost-were used to predict depression risk over three years, incorporating 10 demographic, 5 health, 13 chronic disease, 3 lifestyle, and 2 physical function factors. Lasso feature selection identified 10 key variables for model training. Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, calibration, and decision curve analysis. Among all evaluated models, Boosted XGBoost demonstrated the highest predictive accuracy in the test set (AUC = 0.893), outperforming both Gradient Boosting (AUC = 0.887) and Random Forest (AUC = 0.861). However, Random Forest (RF) achieved superior sensitivity. Consequently, we performed feature importance analysis using both Boosted XGBoost and RF models. The results identified five significant predictors of depression in older adults with subjective cognitive decline (SCD): educational attainment, digestive health status, arthritis diagnosis, sleep duration, and residential location.The machine learning model developed in our study demonstrates strong predictive performance for depression risk among older adults with subjective cognitive decline (SCD), enabling early identification of high-risk individuals. These findings provide a scientific foundation for understanding depression progression mechanisms and developing personalized intervention strategies.
Key findings
- Among all evaluated models, Boosted XGBoost demonstrated the highest predictive accuracy in the test set (AUC = 0.893), outperforming both Gradient Boosting (AUC = 0.887) and Random Forest (AUC = 0.861).
- The results identified five significant predictors of depression in older adults with subjective cognitive decline (SCD): educational attainment, digestive health status, arthritis diagnosis, sleep duration, and residential location.The machine learning model developed in our study demonstrates strong predictive performance for depression risk among older adults with subjective cognitive decline (SCD), enabling early identification of high-risk individuals.
- These findings provide a scientific foundation for understanding depression progression mechanisms and developing personalized intervention strategies.
Why it matters
This study developed a machine learning model to predict depression risk in older adults with subjective cognitive decline, with Boosted XGBoost achieving the highest accuracy. This could enable timely interventions for depression, but no treatment for Alzheimer's disease is tested.
Study details
- Study designlongitudinal study
- Effect sizeAUC = 0.893; AUC = 0.887; AUC = 0.861
- Evidence strengthModerate
How this study scored 51/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific Reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 3.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Wenli Liu: h-index 30 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Shanxi Medical University: institution h-index 258 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40707547) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-025-10645-3
- PubMed: 40707547
- Original source →
- Free full text: Scientific Reports →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Xu, J., Zhang, W., & Liu, W. (2025). A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study. Scientific Reports. https://doi.org/10.1038/s41598-025-10645-3
Vancouver
Xu J, Zhang W, Liu W. A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study. Scientific Reports. 2025. doi:10.1038/s41598-025-10645-3. PMID: 40707547.
BibTeX
@article{xu2025-40707547,
author = {Xu, Jing and Zhang, Wenjin and Liu, Wenli},
title = {{A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study}},
journal = {Scientific Reports},
year = {2025},
doi = {10.1038/s41598-025-10645-3},
pmid = {40707547},
url = {https://doi.org/10.1038/s41598-025-10645-3}
}
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