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Updated: Mar 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Health Variables Are Informative in Screening for Mild Cognitive Impairment Among Elderly African Americans
Siny Tsang1, Scott A Sperling2, Moon-Ho Park3
11 Columbia University, New York, NY, USA.
Abstract:
To aid primary care providers in identifying people at increased risk for cognitive decline, we explored the relative importance of health and demographic variables in detecting potential cognitive impairment using the Mini-Mental State Examination (MMSE). Participants were 94 older African Americans coming to see their primary care physicians for reasons other than cognitive complaints. Education was strongly associated with cognitive functioning. Among those with at least 9 years of education, patients with more vascular risk factors were at greater risk for mild cognitive impairment. For patients with fewer than 9 years of education, those with fewer prescribed medications were at increased risk for dementia. These results suggest that in addition to the MMSE, primary care physicians can make use of patients' health information to improve identification of patients at increased risk for cognitive impairment. With improved identification, physicians can implement strategies to mitigate the progression and impact of cognitive difficulties.
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