Home › Research › Digital health tools
Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis
Yeliz ŞENKAYA, Çetin Kurnaz, Ferdi Özbilgin. Diagnostics, 2025-08-29
Topic: Amyloid, tau and blood-test diagnosis
The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes.
Key findings
- The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods.
- These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes.
Why it matters
This study reports that a machine learning method using EEG data could help diagnose Alzheimer's disease early, which may allow timely clinical intervention. It is a diagnostic approach only and does not test any treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants342
- Effect size96.01%
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Diagnostics) |
| Journal's citation record | 1 / 4 | Diagnostics: journal h-index 129 (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 | 2 / 4 | 342 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 2 / 7 | Cited 8 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 4.6× 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 | 1 / 4 | Cited 8 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Ferdi Özbilgin: h-index 6 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Ondokuz Mayıs University: institution h-index 193 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40941677) |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 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. Check the citation against the original before you use it.
APA 7th
ŞENKAYA, Y., Kurnaz, Ç., & Özbilgin, F. (2025). Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis. Diagnostics. https://doi.org/10.3390/diagnostics15172190
Vancouver
ŞENKAYA Y, Kurnaz Ç, Özbilgin F. Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis. Diagnostics. 2025. doi:10.3390/diagnostics15172190. PMID: 40941677.
BibTeX
@article{senkaya2025-40941677,
author = {ŞENKAYA, Yeliz and Kurnaz, Çetin and Özbilgin, Ferdi},
title = {{Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis}},
journal = {Diagnostics},
year = {2025},
doi = {10.3390/diagnostics15172190},
pmid = {40941677},
url = {https://doi.org/10.3390/diagnostics15172190}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Related studies in Digital health tools
Also tagged “classification”
835 studies in the library carry the topic tag “classification”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.