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Updated: May 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Clustering mild cognitive impairment by mini-mental state examination
So Young Kim1, Tae Sung Lim, Hyun Young Lee
1Department of Neurology, School of Medicine, Ajou University, 5 San, Woncheon-dong, Yongtong-gu, Suwon-si, Kyunggi-do, 442-749, Republic of Korea.
The Mini-Mental State Examination (MMSE) can identify individuals with mild cognitive impairment (MCI) at higher risk for dementia. Clustering MMSE performance helps stratify MCI subtypes for better dementia risk prediction.
Area of Science:
- Neuroscience
- Gerontology
- Cognitive Psychology
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia.
- Accurate identification of MCI subtypes and their progression risk is crucial for timely intervention.
- The Mini-Mental State Examination (MMSE) is a widely used cognitive screening tool.
Purpose of the Study:
- To evaluate the Mini-Mental State Examination's (MMSE) ability to identify high-risk mild cognitive impairment (MCI) subtypes.
- To explore clustering of MMSE performance with demographic factors for predicting dementia conversion.
- To assess the utility of MMSE-based clustering as a screening tool for MCI.
Main Methods:
- Recruited 122 amnestic MCI-single domain (ASM), 303 amnestic MCI-multiple domains (AMM), and 94 non-amnestic MCI (NAM) participants.
- Employed two-step cluster and linear discriminant analyses to identify MMSE performance clusters based on age and education.
- Compared dementia conversion rates among identified clusters using odds ratios.
Main Results:
- Three distinct clusters emerged: Cluster 1 (205 AMM), Cluster 2 (61 NAM, 122 ASM), and Cluster 3 (33 NAM, 98 AMM).
- Cluster 3 exhibited significantly lower performance in orientation, registration, attention/calculation, language, and visuospatial skills compared to Clusters 1 and 2.
- Cluster 1 showed the highest conversion rate to dementia (OR = 2.940 vs. Cluster 2; OR = 2.271 vs. Cluster 3), particularly in delayed recall.
- Cluster 1 demonstrated the most significant conversion into dementia.
Conclusions:
- Clustering MMSE performance, alongside age and education, can effectively stratify MCI patients based on dementia risk.
- The MMSE, through performance clustering, shows promise as a screening and subtyping tool for MCI, especially when comprehensive neuropsychological testing is not feasible.
- This approach aids in identifying individuals with MCI who are at a higher risk of progressing to dementia.
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