Home › Research › Digital health tools
A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study
Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H.. Journal of medical Internet research, 2021-02-24
Topic: Risk, prevention and care in population studies
Background Identifying cognitive impairment early enough could support timely intervention that may hinder or delay the trajectory of cognitive impairment, thus increasing the chances for successful cognitive aging. Objective We aimed to build a prediction model based on machine learning for cognitive impairment among Chinese community-dwelling elderly people with normal cognition. Methods A prospective cohort of 6718 older people from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) register, followed between 2008 and 2011, was used to develop and validate the prediction model. Participants were included if they were aged 60 years or above, were community-dwelling elderly people, and had a cognitive Mini-Mental State Examination (MMSE) score ≥18. They were excluded if they were diagnosed with a severe disease (eg, cancer and dementia) or were living in institutions. Cognitive impairment was identified using the Chinese version of the MMSE. Several machine learning algorithms (random forest, XGBoost, naïve Bayes, and logistic regression) were used to assess the 3-year risk of developing cognitive impairment. Optimal cutoffs and adjusted parameters were explored in validation data, and the model was further evaluated in test data. A nomogram was established to vividly present the prediction model. Results The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female. During a 3-year follow-up, 991 (14.8%) participants were identified with cognitive impairment. Among 45 features, the following four features were finally selected to develop the model: age, instrumental activities of daily living, marital status, and baseline cognitive function. The concordance index of the model constructed by logistic regression was 0.814 (95% CI 0.781-0.846). Older people with normal cognitive functioning having a nomogram score of less than 170 were considered to have a low 3-year risk of cognitive impairment, and those with a score of 170 or greater were considered to have a high 3-year risk of cognitive impairment. Conclusions This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia.
Key findings
- Results The mean age of the participants was 80.4 years (SD 10.3 years), and 50.85% (3416/6718) were female.
Why it matters
This study developed and validated a machine learning model to predict 3-year risk of cognitive impairment among 6,718 Chinese community-dwelling older people with normal cognition. It is a risk prediction tool, not a treatment, cure, or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designprospective cohort
- Effect size50.85%; 14.8%
- Evidence strengthModerate
How this study scored 60/100
Published in a peer-reviewed journal, and it is a prospective cohort study, followed forward from enrollment. 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 (Journal of medical Internet research) |
| Journal's citation record | 3 / 4 | Journal of Medical Internet Research: journal h-index 286 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A prospective cohort study, followed forward from enrollment |
| 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 | 3 / 5 | 2021 — within 6 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 5 / 7 | Cited 139 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 19.9 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 4 / 6 | Cited 11.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 3 / 4 | Cited 110 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Hui Feng: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Central South University: institution h-index 699 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33625369) |
| Free to read | 2 / 2 | Free to read |
| Total | 60 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.2196/20298
- PubMed: 33625369
- Original source →
- Free full text: Journal of Medical Internet Research →
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
Hu, M., Shu, X., Yu, G., Wu, X., Välimäki, M., & Feng, H. (2021). A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. Journal of medical Internet research. https://doi.org/10.2196/20298
Vancouver
Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. Journal of medical Internet research. 2021. doi:10.2196/20298. PMID: 33625369.
BibTeX
@article{hu2021-33625369,
author = {Hu, M. and Shu, X. and Yu, G. and Wu, X. and Välimäki, M. and Feng, H.},
title = {{A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study}},
journal = {Journal of medical Internet research},
year = {2021},
doi = {10.2196/20298},
pmid = {33625369},
url = {https://doi.org/10.2196/20298}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
4 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also tagged “assessment”
840 studies in the library carry the topic tag “assessment”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 840 tagged studies.