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Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability
Deyan Liu, Yuge Tian, Min Liu, Shangjian Yang. BMC Public Health, 2025-06-02
Topic: Risk, prevention and care in population studies
The results indicated that residence location, alcohol consumption, life satisfaction, depressive symptoms, and education level are key factors influencing the risk of MCI among older adults with functional disability. Among the models, the neural network achieved the best overall performance (Accuracy: 0.71, Precision: 0.70, Recall: 0.74, F1 Score: 0.72, ROC AUC: 0.80) with stable results across both the training and test sets. This study provides a scientific tool for the early screening of MCI in older adults with functional disability and offers an efficient and scalable predictive model for clinical applications and community health services.
Key findings
- The results indicated that residence location, alcohol consumption, life satisfaction, depressive symptoms, and education level are key factors influencing the risk of MCI among older adults with functional disability.
- Among the models, the neural network achieved the best overall performance (Accuracy: 0.71, Precision: 0.70, Recall: 0.74, F1 Score: 0.72, ROC AUC: 0.80) with stable results across both the training and test sets.
- This study provides a scientific tool for the early screening of MCI in older adults with functional disability and offers an efficient and scalable predictive model for clinical applications and community health services.
Why it matters
A neural network model predicted risk of mild cognitive impairment in older adults with functional disability, achieving an ROC AUC of 0.80, with residence location, alcohol consumption, life satisfaction, depressive symptoms and education level as key factors. This is an early screening tool, not a treatment or prevention for Alzheimer's disease.
Study details
- Study designlongitudinal study
- Effect sizeAUC: 0.80
- Evidence strengthModerate
How this study scored 46/100
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 (BMC Public Health) |
| Journal's citation record | 3 / 4 | BMC Public Health: journal h-index 289 (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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 0 / 4 | Last-listed author Shangjian Yang: h-index 4 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Shandong University: institution h-index 615 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40457220) |
| Free to read | 2 / 2 | Free to read |
| Total | 46 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1186/s12889-025-23310-1
- PubMed: 40457220
- Original source →
- Free full text: BMC Public Health →
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
Liu, D., Tian, Y., Liu, M., & Yang, S. (2025). Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability. BMC Public Health. https://doi.org/10.1186/s12889-025-23310-1
Vancouver
Liu D, Tian Y, Liu M, Yang S. Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability. BMC Public Health. 2025. doi:10.1186/s12889-025-23310-1. PMID: 40457220.
BibTeX
@article{liu2025-40457220,
author = {Liu, Deyan and Tian, Yuge and Liu, Min and Yang, Shangjian},
title = {{Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability}},
journal = {BMC Public Health},
year = {2025},
doi = {10.1186/s12889-025-23310-1},
pmid = {40457220},
url = {https://doi.org/10.1186/s12889-025-23310-1}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.