Diagnosis of Alzheimer’s disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms
Abstract
Background: Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning‐based models for the diagnosis using cognitive tests. Methods: Three hundred and twenty‐nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono‐objective and multi‐objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta‐model strategy. Results: Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. Conclusions: Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.
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Supervised ML models, particularly SVMs, achieved 84.5-92.6% accuracy in distinguishing FTD from AD and healthy controls by leveraging combinations of neuropsychological assessments, PET imaging, and demographic data.
“Supervised ML models, particularly SVMs, have achieved high accuracy (84.5–92.6%) in distinguishing FTD from AD and healthy controls by leveraging combinations of neuropsychological assessments, PET imaging, and demographic data”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Garcia‐Gutierrez et al., 2022
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- Authors
- Garcia‐Gutierrez, F., Delgado‐Alvarez, A., Delgado‐Alonso, C., Díaz‐Álvarez, J., Pytel, V., Valles‐Salgado, M., Gil, M. J., Hernández‐Lorenzo, L., Matías‐Guiu, J., Ayala, J. L., & Matias‐Guiu, J. A.
- Affiliations
- Department of Neurology Hospital Clínico San Carlos San Carlos Research Health Institute (IdISSC) Universidad Complutense Madrid Spain
- Universidad Complutense de Madrid (ES)
- Department of Computer Architecture and Communications Centro Universitario de Mérida Universidad de Extremadura Merida Spain
- Corresponding author
- Matias‐Guiu, Jordi A.
- Venue
- International Journal of Geriatric Psychiatry
- Publisher
- Wiley
- Published
- 2022-01-11
- Volume
- 37
- Issue
- 2
- ISSN
- 0885-6230 (ISSN-L)
- 1099-1166
- OpenAlex
- W4200279816
- Cited by
- 56
- References
- 51
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 17 · 2023: 11 · 2022: 8
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Geriatric Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- European Regional Development Fund
- Junta de Extremadura
- Spanish Ministry of Science and Innovation
- Ministerio de Economía y Competitividad
- Instituto de Salud Carlos III
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Clinical Psychology › Historical and Modern Theater Studies
- Keywords
- Alzheimer's disease, behavioral variant frontotemporal dementia, differential diagnosis, neuropsychological assessment, machine learning classification, genetic algorithm, feature selection, cognitive tests, Free and Cued Selective Reminding Test, verbal fluency, Addenbrooke's Cognitive Examination, meta-model, neurodegenerative diseases
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