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Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study
Grace J. Goodwin, Jorge Ramón Fonseca Cacho, Sebastian Mehrzad, Jeffrey L. Cummings, Samantha E. John. Alzheimer s & Dementia, 2025-10-01
Topic: Risk, prevention and care in population studies
Among 1616 participants (AD = 1498, bvFTD = 118), all algorithms achieved high accuracy (80% to 90%) and discriminatory ability (AUC = 0.89 to 0.95). Apathy, disinhibition, and digit-symbol substitution were the most important classification features. Findings emphasize the value of specific clinical disease markers to support differential diagnosis of AD and bvFTD. HIGHLIGHTS: Four ML algorithms were used for the classification of AD and bvFTD. Neuropsychological subtests and neuropsychiatric symptoms were input features. Models had high classification accuracy and discrimination. We identified important and accessible clinical features for classification.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Goodwin, G. J., Cacho, J. R. F., Mehrzad, S., Cummings, J. L., & John, S. E. (2025). Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70782
Vancouver
Goodwin GJ, Cacho JRF, Mehrzad S, Cummings JL, John SE. Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70782. PMID: 41117394.
BibTeX
@article{goodwin2025-41117394,
author = {Goodwin, Grace J. and Cacho, Jorge Ramón Fonseca and Mehrzad, Sebastian and Cummings, Jeffrey L. and John, Samantha E.},
title = {{Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70782},
pmid = {41117394},
url = {https://doi.org/10.1002/alz.70782}
}
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Key findings
- Among 1616 participants (AD = 1498, bvFTD = 118), all algorithms achieved high accuracy (80% to 90%) and discriminatory ability (AUC = 0.89 to 0.95).
- Apathy, disinhibition, and digit-symbol substitution were the most important classification features.
- Findings emphasize the value of specific clinical disease markers to support differential diagnosis of AD and bvFTD.
- HIGHLIGHTS: Four ML algorithms were used for the classification of AD and bvFTD.
- Neuropsychological subtests and neuropsychiatric symptoms were input features.
Why it matters
Machine learning using neuropsychological and neuropsychiatric variables classified Alzheimer's disease versus behavioral variant frontotemporal dementia with 80% to 90% accuracy and AUC of 0.89 to 0.95. It supports differential diagnosis, not treatment, cure or prevention.
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Study details
- Participants1,616
- Effect sizeAUC = 0.89
- Evidence strengthLow
How this study scored 46/100
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 (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 3 / 4 | 1,616 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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Samantha E. John: h-index 15 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Princeton University: institution h-index 1,563 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41117394) |
| Free to read | 2 / 2 | Free to read |
| Total | 46 / 100 | — |
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