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Updated: May 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The Modified Mini-Mental State Examination test: normative data for Singapore Chinese older adults and its
Lei Feng1, Mei Sian Chong, Wee Shiong Lim
1Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Introduction:
This study aimed to determine the stratified normative data by age and education for a modified version of the Mini-Mental State Examination (MMSE) test from a large sample of community-dwelling Chinese older adults in Singapore, and to examine the MMSE's value in detecting early cognitive impairment.
Methods:
We studied 1,763 Chinese older adults with normal cognitive function and 121 Chinese older adults with early cognitive impairment (Clinical Dementia Rating global score 0.5). Normative MMSE values were derived for each of the 15 strata classified by age (three groups) and education level (five groups). Receiver operating characteristic curve analysis was conducted for the whole sample and each of the three education subgroups (no education, primary, secondary and above).
Results:
Education level and age significantly influenced the normative values of MMSE total scores in Chinese older adults with normal cognitive function. For the purpose of detecting early cognitive impairment, an optimal balance between sensitivity (Se) and specificity (Sp) was obtained at a cutoff score of 25, 27 and 29 for each of the three education groups, respectively. For the whole sample, the optimal cutoff point was 26 (Se 0.61, Sp 0.84, area under curve 0.78).
Conclusion:
Age and education level must be taken into account in the interpretation of optimal cutoffs for the MMSE. Although widely used, the MMSE has limited value in detecting early cognitive impairment; tests with better performance should be considered in clinical practice.

