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Updated: Jul 14, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mini-mental State Examination performance in mild cognitive impairment subtypes
Breno S O Diniz1, Monica S Yassuda, Paula V Nunes
1Laboratory of Neuroscience, Department and Institute of Psychiatry, Faculty of Medicine, University of Sao Paulo, Brazil.
Background:
Diagnosis of mild cognitive impairment (MCI) and its subtypes requires a detailed diagnostic assessment and so may be missed at routine primary-care appointments, since the ordinary dementia screening tests lack sensitivity owing to ceiling effects, especially in highly educated subjects.
Methods:
The study was undertaken using a cross-sectional assessment of 112 elderly subjects (mean age 67.96+/-5.77 years, and mean education level of 12.8+/-5.7 years) with a semi-structured interview and a neuropsychological battery.
Results:
MCI patients did not differ from controls on total MMSE scores (p=0.212). Nevertheless, MCI patients showed worse performance than controls on the verbal memory task (p=0.012), and "drawing a pentagon" (p=0.03). Amnestic MCI patients performed worse only on the "three-word recall" task (p=0.013); non-amnestic MCI patients performed worse on the "three-stage command" task (p=0.001); and multiple-domain MCI patients performed worse on the "drawing a pentagon" task (p=0.001) and had a trend toward performing poorly on the 'three word recall' task (p=0.06).
Conclusion:
The analysis of MMSE subtest scores, in addition to MMSE total scores, may increase the sensitivity of the MMSE test in screening for MCI and its subtypes.
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