Skip to main content

Research Repository

Advanced Search

Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection

Rajab, Mohammed D.; Jammeh, Emmanuel; Taketa, Teruka; Brayne, Carol; Matthews, Fiona E.; Su, Li; Ince, Paul G.; Wharton, Stephen B.; Wang, Dennis


Mohammed D. Rajab

Emmanuel Jammeh

Teruka Taketa

Carol Brayne

Li Su

Paul G. Ince

Stephen B. Wharton

Dennis Wang


Although a variety of brain lesions may contribute to the pathological assessment of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remains uncertain. Systematically assessing neuropathological measures by their degree of association with dementia may lead to better diagnostic systems and treatment targets. This study aims to apply machine learning approaches to feature selection in order to identify critical features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification to objectively compare neuropathological features and their relationship to dementia status during life using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). We first tested Alzheimer’s Disease and tau markers and then other neuropathologies associated with dementia. Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Although highly correlated, Braak neurofibrillary tangle stage, beta-amyloid and cerebral amyloid angiopathy features were ranked the highest. The best-performing dementia classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity and 75% precision. However, when assessing all seven classifiers and the 22 ranked features, a substantial proportion (40.4%) of dementia cases was consistently misclassified. These results highlight the benefits of using machine learning to identify critical indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for classifying dementia.


Rajab, M. D., Jammeh, E., Taketa, T., Brayne, C., Matthews, F. E., Su, L., …Wang, D. (2023). Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection. Alzheimer's Research and Therapy, 15(1), Article 47.

Journal Article Type Article
Acceptance Date Feb 20, 2023
Online Publication Date Mar 10, 2023
Publication Date Dec 1, 2023
Deposit Date Jan 21, 2024
Publicly Available Date Jan 23, 2024
Journal Alzheimer's Research and Therapy
Electronic ISSN 1758-9193
Publisher BioMed Central
Peer Reviewed Peer Reviewed
Volume 15
Issue 1
Article Number 47
Keywords Dementia; Alzheimer’s; Feature selection; Machine learning; Neuropathology; Beta-amyloid
Public URL


Published article (2 Mb)

Publisher Licence URL

Copyright Statement
© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

You might also like

Downloadable Citations