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Predicting Cognitive Impairment and Dementia: A Machine Learning Approach
Aschwanden D, Aichele S, Ghisletta P, Terracciano A, Kliegel M, Sutin AR, Brown J, Allemand M.. Journal of Alzheimer's disease : JAD, 2020-01-01
Topic: Risk, prevention and care in population studies
Background Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking. Objective We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults. Methods Participants from the Health and Retirement Study (N = 9,979; aged 50-98 years) were followed for up to 10 years (M = 6.85 for cognitive impairment; M = 7.67 for dementia). Using a split-sample methodology, we first estimated the relative importance of predictors using machine learning (random forest survival analysis), and we then used semi-parametric survival analysis (Cox proportional hazards) to estimate effect sizes for the most important variables. Results African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia. Sociodemographic (lower education, Hispanic ethnicity) and health variables (worse subjective health, increasing BMI) were comparatively strong predictors for cognitive impairment. Cardiovascular factors (e.g., smoking, physical inactivity) and polygenic scores (with and without APOEɛ4) appeared less important than expected. Post-hoc sensitivity analyses underscored the robustness of these results. Conclusions Higher-order factors (e.g., emotional distress, subjective health), which reflect complex interactions between various aspects of an individual, were more important than narrowly defined factors (e.g., clinical and behavioral indicators) when evaluated concurrently to predict cognitive impairment and dementia.
Key findings
- Results African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia.
- Post-hoc sensitivity analyses underscored the robustness of these results.
Why it matters
This study used machine learning and survival analysis on 52 predictors in a large population sample to estimate risk of cognitive impairment and dementia. It identified African Americans and those with high emotional distress as at relatively highest risk, but this is about risk prediction, not treatment or cure.
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Study details
- Study designpost-hoc analysis
- Participants9,979
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Alzheimer's disease : JAD) |
| Journal's citation record | 2 / 4 | Journal of Alzheimer s Disease: journal h-index 218 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | Design recorded as “post-hoc analysis”, which our rubric does not rank (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 9,979 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 4 / 7 | Cited 67 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.4 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 5.6× 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 | 2 / 4 | Cited 32 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Mathias Allemand: h-index 47 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Zurich: institution h-index 1,220 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32333585) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3233/jad-190967
- PubMed: 32333585
- Original source →
- Free full text: Zurich Open Repository and Archive (University of Zurich) →
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 names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Aschwanden, D., Aichele, S., Ghisletta, P., Terracciano, A., Kliegel, M., Sutin, A. R., et al. (2020). Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer's disease : JAD. https://doi.org/10.3233/jad-190967
Vancouver
Aschwanden D, Aichele S, Ghisletta P, Terracciano A, Kliegel M, Sutin AR, et al. Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer's disease : JAD. 2020. doi:10.3233/jad-190967. PMID: 32333585.
BibTeX
@article{aschwanden2020-32333585,
author = {Aschwanden, D. and Aichele, S. and Ghisletta, P. and Terracciano, A. and Kliegel, M. and Sutin, A. R. and Brown, J. and Allemand, M.},
title = {{Predicting Cognitive Impairment and Dementia: A Machine Learning Approach}},
journal = {Journal of Alzheimer's disease : JAD},
year = {2020},
doi = {10.3233/jad-190967},
pmid = {32333585},
url = {https://doi.org/10.3233/jad-190967}
}
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