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Updated: Dec 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
MMSE Subscale Scores as Useful Predictors of AD Conversion in Mild Cognitive Impairment
Young Min Choe1,2, Boung Chul Lee2,3, Ihn-Geun Choi4
1Department of Neuropsychiatry, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Gyeonggi, Republic of Korea.
Purpose:
This study was performed to examine the usefulness of subscores on the Mini-Mental State Examination (MMSE) for predicting the progression of Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI).
Patients And Methods:
A total of 306 MCI individuals in the Alzheimer's Disease Neuroimaging Initiative database were included in the study. Standardized clinical and neuropsychological tests were performed at baseline and at 2-year follow-up. Logistic regression analysis was conducted to examine the MMSE total and subscale scores to predict progression to AD dementia in MCI individuals.
Results:
The MMSE total score and the MMSE memory, orientation, and construction subscores were inversely associated with AD progression after controlling for all potential confounders; MMSE attention and language subscores were not correlated with AD progression. The MMSE delayed recall score among the MMSE memory subscores and the MMSE time score among the orientation subscores, especially week and day, were inversely associated with AD progression; the MMSE immediate recall and place scores were not correlated with progression.
Conclusion:
Our findings suggest that the MMSE memory, orientation, and construction subscores, which are simple and readily available clinical measures, could provide useful information to predict AD dementia progression in MCI individuals in practical clinical settings.
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