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Updated: Sep 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development of the CogDrisk tool to assess risk factors for dementia
Kaarin J Anstey1,2,3, Scherazad Kootar1,2,3, Md Hamidul Huque1,2,3
1School of Psychology Matthews Building University of New South Wales Kensington New South Wales Australia.
Introduction:
We aimed to develop a comprehensive risk assessment tool for Alzheimer's disease (AD), vascular dementia (VaD), and any dementia, that will be applicable in high and low resource settings.
Method:
Risk factors which can easily be assessed in most settings, and their effect sizes, were identified from an umbrella review, or estimated using meta-analysis where new data were available.
Results:
Seventeen risk/protective factors met criteria for the algorithm to estimate risk for any dementia including age, sex, education, hypertension, midlife obesity, midlife high cholesterol, diabetes, insufficient physical activity, depression, traumatic brain injury, atrial fibrillation, smoking, social engagement, cognitive engagement, fish consumption (diet), stroke, and insomnia. A version for AD excluded atrial fibrillation and insomnia due to insufficient evidence and included pesticide exposure. There was insufficient evidence for a VaD risk score.
Discussion:
Validation of the tool on external datasets is planned. The assessment tool will assist with implementing risk reduction guidelines.
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