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Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions
Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P.. Journal of medical systems, 2023-02-01
Topic: Risk, prevention and care in population studies
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
Key findings
- It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data.
Why it matters
This systematic review of machine learning for dementia prediction found that image data driven models yielded promising results compared with clinical feature-based and voice data. It concerns automated diagnostic systems, not treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 62/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of medical systems) |
| Journal's citation record | 1 / 4 | Journal of Medical Systems: journal h-index 143 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question (from PubMed type “Systematic Review”) |
| 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 | 3 / 5 | 2023 — within 6 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 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 186 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 37.2 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 11.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 3 / 4 | Cited 166 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Peter Anderberg: h-index 25 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36720727) |
| Free to read | 2 / 2 | Free to read |
| Total | 62 / 100 | — |
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Read the original
- DOI: 10.1007/s10916-023-01906-7
- PubMed: 36720727
- Original source →
- Free full text: Journal of Medical Systems →
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
Javeed, A., Dallora, A. L., Berglund, J. S., Ali, A., Ali, L., & Anderberg, P. (2023). Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems. https://doi.org/10.1007/s10916-023-01906-7
Vancouver
Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems. 2023. doi:10.1007/s10916-023-01906-7. PMID: 36720727.
BibTeX
@article{javeed2023-36720727,
author = {Javeed, A. and Dallora, A. L. and Berglund, J. S. and Ali, A. and Ali, L. and Anderberg, P.},
title = {{Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions}},
journal = {Journal of medical systems},
year = {2023},
doi = {10.1007/s10916-023-01906-7},
pmid = {36720727},
url = {https://doi.org/10.1007/s10916-023-01906-7}
}
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