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Explainable AI-based Alzheimer's prediction and management using multimodal data
Jahan S, Abu Taher K, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, Ra IH.. PloS one, 2023-11-16
Topic: Amyloid, tau and blood-test diagnosis
Background According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world's elderly people. Day by day the number of Alzheimer's patients is rising. Considering the increasing rate and the dangers, Alzheimer's disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. Objective To solve these issues, this paper proposes a novel explainable Alzheimer's disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer's disease. Method For predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work. Results and conclusions The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.
Key findings
- Results and conclusions The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others.
Why it matters
This study combined clinical, MRI, and psychological data with machine learning to classify five categories including Alzheimer's disease, reporting 98.81% accuracy with a Random Forest classifier. It is about improving diagnosis, not about treating, curing, or preventing Alzheimer's disease.
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Study details
- Effect size98.81%
- Evidence strengthLow
How this study scored 53/100
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 (PloS one) |
| Journal's citation record | 4 / 4 | PLoS ONE: journal h-index 594 (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 | 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 | 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 | 5 / 7 | Cited 116 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 23.2 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 5 / 6 | Cited 20.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 116 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author In-Ho Ra: h-index 23 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Nottingham Trent University: institution h-index 341 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37972072) |
| Free to read | 2 / 2 | Free to read |
| Total | 53 / 100 | — |
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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
Jahan, S., Abu Taher, K., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S. M. S., et al. (2023). Explainable AI-based Alzheimer's prediction and management using multimodal data. PloS one. https://doi.org/10.1371/journal.pone.0294253
Vancouver
Jahan S, Abu Taher K, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, et al. Explainable AI-based Alzheimer's prediction and management using multimodal data. PloS one. 2023. doi:10.1371/journal.pone.0294253. PMID: 37972072.
BibTeX
@article{jahan2023-37972072,
author = {Jahan, S. and Abu Taher, K. and Kaiser, M. S. and Mahmud, M. and Rahman, M. S. and Hosen, A. S. M. S. and Ra, I. H.},
title = {{Explainable AI-based Alzheimer's prediction and management using multimodal data}},
journal = {PloS one},
year = {2023},
doi = {10.1371/journal.pone.0294253},
pmid = {37972072},
url = {https://doi.org/10.1371/journal.pone.0294253}
}
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