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Machine-learning techniques for building a diagnostic model for very mild dementia
Chen R, Herskovits EH.. NeuroImage, 2010-04-09
Topic: Risk, prevention and care in population studies
Many researchers have sought to construct diagnostic models to differentiate individuals with very mild dementia (VMD) from healthy elderly people, based on structural magnetic-resonance (MR) images. These models have, for the most part, been based on discriminant analysis or logistic regression, with few reports of alternative approaches. To determine the relative strengths of different approaches to analyzing structural MR data to distinguish people with VMD from normal elderly control subjects, we evaluated seven different classification approaches, each of which we used to generate a diagnostic model from a training data set acquired from 83 subjects (33 VMD and 50 control). We then evaluated each diagnostic model using an independent data set acquired from 30 subjects (13 VMD and 17 controls). We found that there were significant performance differences across these seven diagnostic models. Relative to the diagnostic models generated by discriminant analysis and logistic regression, the diagnostic models generated by other high-performance diagnostic-model-generation algorithms manifested increased generalizability when diagnostic models were generated from all atlas structures.
Key findings
- We found that there were significant performance differences across these seven diagnostic models.
Why it matters
This study compared seven classification methods for distinguishing very mild dementia from healthy aging using structural MRI and found significant performance differences among them. It is about diagnostic modeling and does not report any treatment, cure, or prevention.
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Study details
- Participants83
- Evidence strengthLow
How this study scored 44/100
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 (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (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 | 1 / 5 | 2010 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 83 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 68 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.8 citations a year over 18 year(s) since 2010 |
| Impact for its field and year | 2 / 6 | Cited 1.8× 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 | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Edward H. Herskovits: h-index 31 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Pennsylvania: institution h-index 1,859 (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 20382237) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 44 / 100 | — |
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Read the original
- DOI: 10.1016/j.neuroimage.2010.03.084
- PubMed: 20382237
- Original source →
- Free full text: PubMed Central →
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
Chen, R., & Herskovits, E. H. (2010). Machine-learning techniques for building a diagnostic model for very mild dementia. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.03.084
Vancouver
Chen R, Herskovits EH. Machine-learning techniques for building a diagnostic model for very mild dementia. NeuroImage. 2010. doi:10.1016/j.neuroimage.2010.03.084. PMID: 20382237.
BibTeX
@article{chen2010-20382237,
author = {Chen, R. and Herskovits, E. H.},
title = {{Machine-learning techniques for building a diagnostic model for very mild dementia}},
journal = {NeuroImage},
year = {2010},
doi = {10.1016/j.neuroimage.2010.03.084},
pmid = {20382237},
url = {https://doi.org/10.1016/j.neuroimage.2010.03.084}
}
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