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Updated: May 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Proactive approach in detecting elderly subjects with cognitive decline in general practitioners' practices
M Hanzevacki1, G Ozegovic, I Simovic
1Health Care Center Zagreb Zapad, Zagreb, Croatia.
Background:
Although cognitive decline is a common finding among the elderly and is considered a risk factor for developing dementia, it is rarely diagnosed by general practitioners (GPs).
Aim:
To evaluate cognitive function with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in asymptomatic subjects in daily GP practice and compare subjects who confirmed having cognitive problems with subjects who did not.
Methods:
388 consecutive subjects >65 years of age who consulted their GP were interviewed and tested with MMSE and MoCA.
Results:
None of the study subjects spontaneously complained of cognitive or memory problems. 155 subjects (39.94%) confirmed having cognitive problems and 233 (60.05%) did not even when asked. The prevalence of mild cognitive impairment (MCI) was 18.30% (95% CI 14.36-22.04) and the prevalence of cognitive impairment/no dementia (CIND) was 17.27% (95% CI 13.50-21.04). Delayed memory recall as a separate cognitive domain in MoCA was significantly worse in subjects with MCI (p = 0.00958) and in those with CIND (p = 0.0208).
Conclusion:
There is a significant number of patients in daily GP practices with unrecognized, but objectively verifiable, cognitive deficits who do not report having cognitive problems. They can be identified by assessment with MMSE and MoCA already in the GP practice.
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