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Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection
Fadi Thabtah, David Peebles. Health and Technology, 2025-08-08
Topic: Risk, prevention and care in population studies
Abstract Purpose Dementia is a condition with symptoms of memory decline, cognitive impairment, and difficulties in language and problem-solving, among others. Early screening of dementia conditions such as Alzheimer’s disease (AD) is fundamental for quick intervention and disease management. Currently used neuropsychological assessments are time-consuming as they contain many elements and require critical resources which are not always available. Other pathological assessments are invasive and not cost effective, hence identifying cognitive features for different dementia sub-groups during progression of the condition is crucial for clinicians. This study investigates this problem using a cost-effective data driven approach. Methods Using real cases and controls from the Alzheimer’s Disease Neuroimaging Initiative data repository (ADNI) who have undergone the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog), we conduct a feature-feature assessment together with Permutation Feature Importance (PFI) and machine learning algorithms to derive influential cognitive features for specific dementia groups from baseline diagnosis up to 36 months. Results Feature-feature analysis showed correlations between memory tasks such as Word Recall, Delayed Word Recall, and Word Recognition across both CN-MCI and MCI-AD groups. In contrast, low correlations for Naming, Command, and Ideational Praxis suggest they tap into distinct DSM-5 domains thus making them ideal for early screening. In addition, PFI results showed that Delayed Word Recall emerged as a top cognitive marker of progression in early stages, while Orientation gained prominence later thereby reflecting a shift toward executive and attentional decline. Conclusions The results of this study identified important relationships between cognitive features in the ADAS-Cog and provide a clear example of the value of data-driven machine learning approaches in the identification of markers that indicate disease progression in dementia.
Key findings
- Results Feature-feature analysis showed correlations between memory tasks such as Word Recall, Delayed Word Recall, and Word Recognition across both CN-MCI and MCI-AD groups.
- In addition, PFI results showed that Delayed Word Recall emerged as a top cognitive marker of progression in early stages, while Orientation gained prominence later thereby reflecting a shift toward executive and attentional decline.
- Conclusions The results of this study identified important relationships between cognitive features in the ADAS-Cog and provide a clear example of the value of data-driven machine learning approaches in the identification of markers that indicate disease progression in dementia.
Why it matters
Using ADNI data, this study identified cognitive features linked to dementia progression, with Delayed Word Recall emerging as a top marker in early stages and Orientation becoming more prominent later. The aim is to find cost-effective cognitive features for screening and monitoring progression, not to treat, cure, or prevent Alzheimer's disease.
Study details
- Evidence strengthLow
How this study scored 33/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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 (Health and Technology) |
| Journal's citation record | 0 / 4 | Health and Technology: journal h-index 56 (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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.5× 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 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author David Peebles: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Huddersfield: institution h-index 265 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 33 / 100 | — |
What each of these 20 components measures, and what this score is not →
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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. Check the citation against the original before you use it.
APA 7th
Thabtah, F., & Peebles, D. (2025). Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection. Health and Technology. https://doi.org/10.1007/s12553-025-01006-1
Vancouver
Thabtah F, Peebles D. Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection. Health and Technology. 2025. doi:10.1007/s12553-025-01006-1.
BibTeX
@article{thabtah2025-d6e5e6fc,
author = {Thabtah, Fadi and Peebles, David},
title = {{Cognitive feature evaluation for disease progression in dementia and its precursors using feature selection}},
journal = {Health and Technology},
year = {2025},
doi = {10.1007/s12553-025-01006-1},
url = {https://doi.org/10.1007/s12553-025-01006-1}
}
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