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Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study
Hao Ren, Yiying Zheng, Changjin Li, Fengshi Jing, Qiting Wang, Zeyu Luo, Dongxiao Li, Deyi Liang et al.. JMIR Aging, 2025-04-30
Topic: Risk, prevention and care in population studies
All ML models outperformed the MMSE alone. The balanced random forest achieved the highest accuracy (88.5% in the internal test set and 88.7% in the prospective validation set), albeit with a lower sensitivity, while logistic regression recorded the highest sensitivity. SHAP (Shapley Additive Explanations) analysis identified instrumental activities of daily living, age, and baseline MMSE scores as the most influential predictors for cognitive impairment. The incorporation of blood biomarkers, along with demographic, life behavior, and disease history into ML models offers a convenient, rapid, and accurate approach for the early identification of older adult individuals at risk of cognitive impairment. This method presents a valuable tool for health care professionals to facilitate timely interventions and underscores the importance of integrating diverse data types in predictive health models.
Key findings
- All ML models outperformed the MMSE alone.
- The balanced random forest achieved the highest accuracy (88.5% in the internal test set and 88.7% in the prospective validation set), albeit with a lower sensitivity, while logistic regression recorded the highest sensitivity.
- SHAP (Shapley Additive Explanations) analysis identified instrumental activities of daily living, age, and baseline MMSE scores as the most influential predictors for cognitive impairment.
- The incorporation of blood biomarkers, along with demographic, life behavior, and disease history into ML models offers a convenient, rapid, and accurate approach for the early identification of older adult individuals at risk of cognitive impairment.
- This method presents a valuable tool for health care professionals to facilitate timely interventions and underscores the importance of integrating diverse data types in predictive health models.
Why it matters
Machine learning models using blood biomarkers, demographics, life behavior, and disease history predicted cognitive impairment more accurately than the MMSE alone. This may help identify older adults at risk for timely interventions, but the study does not test any treatment or prevention.
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Study details
- Study designlongitudinal study
- Participants1,331
- Effect size88.5%; 88.7%
- Evidence strengthModerate
How this study scored 57/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JMIR Aging) |
| Journal's citation record | 0 / 4 | JMIR Aging: journal h-index 49 (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 | 3 / 4 | 1,331 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 11 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 7.0× 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 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Weibin Cheng: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of North Carolina at Chapel Hill: institution h-index 1,674 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40305830) |
| Free to read | 2 / 2 | Free to read |
| Total | 57 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Ren, H., Zheng, Y., Li, C., Jing, F., Wang, Q., Luo, Z., et al. (2025). Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study. JMIR Aging. https://doi.org/10.2196/67437
Vancouver
Ren H, Zheng Y, Li C, Jing F, Wang Q, Luo Z, et al. Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study. JMIR Aging. 2025. doi:10.2196/67437. PMID: 40305830.
BibTeX
@article{ren2025-40305830,
author = {Ren, Hao and Zheng, Yiying and Li, Changjin and Jing, Fengshi and Wang, Qiting and Luo, Zeyu and Li, Dongxiao and Liang, Deyi and others},
title = {{Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study}},
journal = {JMIR Aging},
year = {2025},
doi = {10.2196/67437},
pmid = {40305830},
url = {https://doi.org/10.2196/67437}
}
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