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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.
This study introduces a novel method using the Mini-Mental State Examination (MMSE) to accurately detect depression in elderly individuals. The MMSE writing analysis shows high sensitivity and specificity for identifying depression without additional burden.
Area of Science:
- Geriatric Psychiatry
- Cognitive Assessment
- Mental Health Screening
Background:
- Depression is a significant concern in the elderly population.
- Accurate and non-invasive screening tools are crucial for early intervention.
- The Mini-Mental State Examination (MMSE) is widely used for cognitive assessment.
Purpose of the Study:
- To evaluate a projective method using the MMSE for depression assessment in cognitively healthy older adults.
- To determine the efficacy of MMSE-based writing analysis in differentiating depressed individuals.
Main Methods:
- Cognitively healthy elderly participants were divided into normal, depressed tendency, and depressed groups.
- All participants completed the Mini-Mental State Examination (MMSE) and the Geriatric Depression Scale (Japanese version).
- MMSE writing analysis focused on sentence calculation, writing content, and figure copying.
Main Results:
- Depressed individuals exhibited higher scores for specific writing characteristics: low calculation scores (0 or 1), superior sentence writing relative to peers, and expression of feelings in writing.
- A scoring system was developed where depressed subjects achieved a score of 2 or higher.
- The method demonstrated high accuracy in differentiating depressed subjects.
Conclusions:
- The developed MMSE-based evaluation method accurately identifies depression in the elderly.
- This approach offers high sensitivity (77.8%) and specificity (76.4%).
- The method is non-intrusive, adding no extra burden to the participants.
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