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Updated: Dec 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
An approach to classifying subjective cognitive decline in community-dwelling elders
Laura A Rabin1,2,3, Cuiling Wang3,4, Jacqueline A Mogle5
1Department of Psychology Brooklyn College City University of New York (CUNY) Brooklyn New York USA.
Subjective cognitive decline (SCD) can predict future cognitive impairment. This study offers a method to classify SCD, aiding research in Alzheimer's disease and cognitive aging.
Area of Science:
- Gerontology
- Neuroscience
- Epidemiology
Background:
- Subjective cognitive decline (SCD) is a potential early indicator of Alzheimer's disease.
- Current research lacks standardized methods for classifying SCD in community settings.
Purpose of the Study:
- To examine the association between self-perceived cognitive functioning and incident amnestic mild cognitive impairment (aMCI).
- To develop and validate an approach for classifying SCD in longitudinal studies.
Main Methods:
- Utilized Cox models on data from 1115 neuropsychologically intact older adults.
- Assessed baseline self-perceived cognitive functioning using three distinct methods.
- Controlled for covariates including age, sex, education, race/ethnicity, general cognition, and depressive symptoms.
Main Results:
- Self-perceived cognitive functioning consistently predicted incident aMCI over a median follow-up of 3 years.
- Age, general cognition, and perceived control were also significantly associated with aMCI.
- Established optimal cut points for SCD classification to enhance diagnostic accuracy.
Conclusions:
- The study provides a novel approach for classifying SCD in cognitive aging research.
- Findings have implications for improving the design and interpretation of studies on cognitive decline and dementia.
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