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Updated: Jan 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Evaluating depression in cognitively healthy elderly people by using Mini-Mental State Examination
Norio Murayama1, Kazumi Ota2, Yusuke Matsunaga3
1Faculty of Health and Sports Science, Juntendo University, Inzai, Japan.
Aim:
We examined a method for evaluating depression with the Mini-Mental State Examination in cognitively healthy elderly people and employed the projective perspective.
Methods:
In MMSE three groups-normal, depressed tendency, and depressed-completed the Mini-Mental State Examination (MMSE) and a Japanese version of the 15-item Geriatric Depression Scale. The Mini-Mental State Examination evaluated individuals' writing based on a sentence, the number of written words, and sentence content; it also assessed their copying of drawn figures.
Results:
In the depressed group, the proportion corresponding to the characteristics of (i) to (iii) was higher than in the other two groups: (i) the calculation score was 0 or 1; (ii) subjects scored above the median in sentence writing relative to similar subjects with the same language and clinical setting; and (iii) subjects expressed feelings in their writing. One point was given for each characteristic, and we calculated the sum. Depressed subjects had a score ≥2.
Conclusions:
This evaluation method can differentiate depressed subjects with high accuracy (sensitivity: 77.8%, specificity: 76.4%) without placing an extra burden on the subjects.
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