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Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study
Yuqi Li, Tingting Hou, Jiaqi Dong, Cuicui Liu, Na Tian, Min Zhu, Yi Dong, Jiafeng Wang et al.. Age and Ageing, 2025-03-30
Topic: Risk, prevention and care in population studies
Of the 5200 participants (mean age = 71.0 years, 57.1% females), 302 (5.8%) were diagnosed with dementia. The AUC of LR, RF and XGBoost models for detecting dementia were 0.88 [95% confidence interval (CI): 0.85-0.91], 0.88 (0.84-0.90) and 0.95 (0.94-0.98), respectively, in the validation set (n = 2080). Calibration plots and Brier scores showed that in the validation set, the three models had good overall alignment between the actual and predicted risks. The DCA showed better net benefit of the three models than the intervention for all or no intervention. We developed and evaluated the performance of LR, RF and XGBoost models for dementia detection in a rural older population in China. All the three models showed excellent discrimination and calibration. The XGBoost model exhibited the best performance and clinical utility in dementia detection. These models are based on information readily available in routine clinical practice, which could be easily implemented amongst rural older populations for dementia detection.
Key findings
- Of the 5200 participants (mean age = 71.0 years, 57.1% females), 302 (5.8%) were diagnosed with dementia.
- The AUC of LR, RF and XGBoost models for detecting dementia were 0.88 [95% confidence interval (CI): 0.85-0.91], 0.88 (0.84-0.90) and 0.95 (0.94-0.98), respectively, in the validation set (n = 2080).
- Calibration plots and Brier scores showed that in the validation set, the three models had good overall alignment between the actual and predicted risks.
- The DCA showed better net benefit of the three models than the intervention for all or no intervention.
- We developed and evaluated the performance of LR, RF and XGBoost models for dementia detection in a rural older population in China.
Why it matters
This population-based study developed machine learning models using routinely available clinical information that showed excellent discrimination for detecting dementia in a rural older population in China. It is a detection tool only and does not test any treatment, cure or prevention.
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Study details
- Participants2,080
- Effect size57.1%; 5.8%
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Age and Ageing) |
| Journal's citation record | 2 / 4 | Age and Ageing: journal h-index 231 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” 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 | 2,080 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× 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 | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Chengxuan Qiu: h-index 66 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41378853) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 41 / 100 | — |
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Read the original
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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
Li, Y., Hou, T., Dong, J., Liu, C., Tian, N., Zhu, M., et al. (2025). Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study. Age and Ageing. https://doi.org/10.1093/ageing/afaf085
Vancouver
Li Y, Hou T, Dong J, Liu C, Tian N, Zhu M, et al. Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study. Age and Ageing. 2025. doi:10.1093/ageing/afaf085. PMID: 41378853.
BibTeX
@article{li2025-41378853,
author = {Li, Yuqi and Hou, Tingting and Dong, Jiaqi and Liu, Cuicui and Tian, Na and Zhu, Min and Dong, Yi and Wang, Jiafeng and others},
title = {{Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study}},
journal = {Age and Ageing},
year = {2025},
doi = {10.1093/ageing/afaf085},
pmid = {41378853},
url = {https://doi.org/10.1093/ageing/afaf085}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 17.