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Investigating dementia patterns by machine learning and graph-based marker analysis for multiclass dementia diagnosis
Debopom Sutradhar, Md. Abdur Rahman, Nur Mohammad Fahad, Sadman Sakib, A. B. M. Alim Al Islam, Mirjam Jonkman, Sami Azam. Discover Computing, 2026-05-18
Topic: Risk, prevention and care in population studies
Abstract Dementia is a debilitating disease that leads to a gradual loss of memory and other cognitive abilities and tends to develop in people over 60 years of age. The number of people worldwide with dementia is expected to increase from 57.4 million in 2019 to 131.5 million in 2050. Diagnosis of dementia is a critical task, and some biomarkers and psychological and demographic measures have been used to diagnose dementia clinically. The advent of artificial intelligence (AI) has created new opportunities to improve diagnostic accuracy. In this study, we propose an approach based on machine learning and graph analysis to identify important patterns and diagnostic markers for dementia. The novel contributions of this study include a descriptive probability density function (PDF) analysis to examine the distribution of clinically relevant markers across diagnostic groups, providing exploratory insights into group-wise feature behavior. A comprehensive evaluation of ten machine learning (ML) models is conducted under strict subject-level data separation to identify robust classification performance. In addition, feature ranking is assessed using three complementary strategies (Random Forest importance, Chi-Square statistics, and Boruta ranking) to evaluate the consistency and robustness of marker relevance. Out of ten ML models evaluated, the Random Forest achieved the highest accuracy of 94.37%, with a macro F1-score of 86.96%. The result of the ML model is further validated using a graphical analysis of critical markers. The results of this study might help to improve the diagnostic accuracy.
Key findings
- The result of the ML model is further validated using a graphical analysis of critical markers.
- The results of this study might help to improve the diagnostic accuracy.
Why it matters
This study used machine learning and graph analysis to identify patterns and markers for classifying dementia subtypes, with Random Forest performing best among ten models. The authors state the results might help improve diagnostic accuracy, but no treatment or prevention effect is reported.
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Study details
- Effect size94.37%; 86.96%
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design, 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 (Discover Computing) |
| Journal's citation record | 0 / 4 | Discover Computing: journal h-index 21 (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 | 5 / 5 | 2026 — within 1 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 7.6× 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 | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Sami Azam: h-index 44 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Charles Darwin University: institution h-index 293 (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 | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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 7 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
Sutradhar, D., Rahman, M. A., Fahad, N. M., Sakib, S., Islam, A. B. M. A. A., Jonkman, M., et al. (2026). Investigating dementia patterns by machine learning and graph-based marker analysis for multiclass dementia diagnosis. Discover Computing. https://doi.org/10.1007/s10791-026-10153-6
Vancouver
Sutradhar D, Rahman MA, Fahad NM, Sakib S, Islam ABMAA, Jonkman M, et al. Investigating dementia patterns by machine learning and graph-based marker analysis for multiclass dementia diagnosis. Discover Computing. 2026. doi:10.1007/s10791-026-10153-6.
BibTeX
@article{sutradhar2026-ab39e9ad,
author = {Sutradhar, Debopom and Rahman, Md. Abdur and Fahad, Nur Mohammad and Sakib, Sadman and Islam, A. B. M. Alim Al and Jonkman, Mirjam and Azam, Sami},
title = {{Investigating dementia patterns by machine learning and graph-based marker analysis for multiclass dementia diagnosis}},
journal = {Discover Computing},
year = {2026},
doi = {10.1007/s10791-026-10153-6},
url = {https://doi.org/10.1007/s10791-026-10153-6}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.