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

Alzheimer's Research and Therapy, ISSN: 1758-9193, Vol: 15, Issue: 1, Page: 47
2023
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University of Sheffield Reports Findings in Alzheimer Disease (Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection)

2023 MAR 28 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- New research on Neurodegenerative

Article Description

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.

Bibliographic Details

Rajab, Mohammed D; Jammeh, Emmanuel; Taketa, Teruka; Brayne, Carol; Matthews, Fiona E; Su, Li; Ince, Paul G; Wharton, Stephen B; Wang, Dennis; Cognitive Function and Ageing Neuropathology Study Group

Springer Science and Business Media LLC

Neuroscience; Medicine

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