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Updated: Mar 9, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validation of the Chinese Version of Montreal Cognitive Assessment Basic for Screening Mild Cognitive Impairment
Ke-Liang Chen1, Yan Xu1, Ai-Qun Chu2
1Department of Neurology, Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Objectives:
To evaluate the effectiveness of the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC) as a screening tool for detecting mild cognitive impairment (MCI) in Chinese elderly adults.
Design:
Cross-sectional.
Setting:
Huashan Hospital, Shanghai, China.
Participants:
Individuals with MCI (n = 264) and mild Alzheimer's disease (AD) (n = 160) were recruited from the Memory Clinic, Huashan Hospital; cognitively normal controls were recruited from Jinshan Community, Shanghai, China (n = 280).
Measurements:
MoCA-BC scores.
Results:
The MoCA-BC had good criterion-related validity (Pearson correlation coefficient MoCA-BC vs MMSE = 0.787) and reliable internal consistency (Cronbach alpha = 0.807). The optimal cutoff scores for MCI screening were 19 for individuals with no more than 6 years of education, 22 for individuals with 7 to 12 years of education, and 24 for individuals with more than 12 years of education. The MoCA-BC was superior to the MMSE for detecting MCI, with optimal sensitivity and specificity across all education groups using the above cutoff scores.
Conclusion:
The MoCA-BC is a reliable cognitive screening test across all education levels in Chinese elderly adults, with high acceptance and good reliability.
