Home › Research › Digital health tools
Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data
Gill S, Mouches P, Hu S, Rajashekar D, MacMaster FP, Smith EE, Forkert ND, Ismail Z et al.. Journal of Alzheimer's disease : JAD, 2020-01-01
Topic: Risk, prevention and care in population studies
Background Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. Objectives To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. Method Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. Results In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73). Conclusion Baseline NPS, categorized for MBI domain and duration, have prognostic utility in addition to brain morphology measures for predicting diagnosis change using ML. MBI total score, followed by impulse dyscontrol and affective dysregulation were most predictive of future diagnosis.
Key findings
- Results In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume.
- Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73).
Why it matters
In 102 people with normal cognition and 239 with mild cognitive impairment, a machine learning model using mild behavioral impairment score and left hippocampal volume classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy, while a three-class model achieved 58.8% accuracy. The study concerns prediction of follow-up diagnosis, not treating, curing, or preventing Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants102
- Effect size84.4%; 58.8%; AUC=0.73
- Evidence strengthLow
How this study scored 56/100
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 (Journal of Alzheimer's disease : JAD) |
| Journal's citation record | 2 / 4 | Journal of Alzheimer s Disease: journal h-index 218 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 102 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | 5 / 7 | Cited 116 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.5 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 11.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 11 other studies in this library |
| Still being cited | 2 / 4 | Cited 43 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Zahinoor Ismail: h-index 66 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Calgary: institution h-index 1,028 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32250302) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3233/jad-191169
- PubMed: 32250302
- Original source →
- Free full text: Journal of Alzheimer s Disease →
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Gill, S., Mouches, P., Hu, S., Rajashekar, D., MacMaster, F. P., Smith, E. E., et al. (2020). Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data. Journal of Alzheimer's disease : JAD. https://doi.org/10.3233/jad-191169
Vancouver
Gill S, Mouches P, Hu S, Rajashekar D, MacMaster FP, Smith EE, et al. Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data. Journal of Alzheimer's disease : JAD. 2020. doi:10.3233/jad-191169. PMID: 32250302.
BibTeX
@article{gill2020-32250302,
author = {Gill, S. and Mouches, P. and Hu, S. and Rajashekar, D. and MacMaster, F. P. and Smith, E. E. and Forkert, N. D. and Ismail, Z. and others},
title = {{Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data}},
journal = {Journal of Alzheimer's disease : JAD},
year = {2020},
doi = {10.3233/jad-191169},
pmid = {32250302},
url = {https://doi.org/10.3233/jad-191169}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 19.
Later studies that cite this one
11 studies in the alzhub library list this one in their references. Showing the 8 highest-scoring.