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Updated: Jun 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Assessing mild cognitive impairment among older African Americans
Alyssa A Gamaldo1, Jason C Allaire, Regina C Sims
1North Carolina State University, NC, USA. agamaldo@gmail.com
Objectives:
To examine the frequency of Mild Cognitive Impairment (MCI) in African American older adults. The study also plans to explore the specific cognitive domains of impairment as well as whether there are differences in demographics, health, and cognitive performance between MCI and normal participants.
Design:
Cross-sectional.
Setting:
Independent-living sample of urban dwelling elders in Baltimore, Maryland.
Participants:
The sample consisted of 554 subjects ranging in age from 50 to 95 (Mean = 68.79 +/- 9.60).
Measurements:
Socio-demographics and health were assessed. Several cognitive measures were administered to assess inductive reasoning, declarative memory, perceptual speed, working memory, executive functioning, language and global cognitive functioning.
Results:
Approximately 22% of participants were considered MCI (i.e. 18% non-amnestic vs. 4% amnestic). A majority of the non-amnestic MCI participants had impairment in one cognitive domain, particularly language and executive function. Individuals classified as non-amnestic MCI were significantly older and had more years of education than normal individuals. The MCI groups were not significantly different than cognitively normal individuals on health factors. Individuals classified as MCI performed significantly worse on global cognitive measures as well as across specific cognitive domains than cognitively normal individuals.
Conclusion:
This study demonstrates that impairment in a non-memory domain may be an early indicator of cognitive impairment, particularly among African Americans.
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