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Updated: Dec 24, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive
Alessandra E Thomann1,2, Manfred Berres3, Nicolai Goettel2,4
1Memory Clinic, University Department of Geriatric Medicine FELIX PLATTER, Burgfelderstrasse 101, CH-4055, Basel, Switzerland.
Background:
The Montreal Cognitive Assessment (MoCA) has good sensitivity for mild cognitive impairment, but specificity is low when the original cut-off (25/26) is used. We aim to revise the cut-off on the German MoCA for its use in clinical routine.
Methods:
Data were analyzed from 496 Memory Clinic outpatients (447 individuals with a neurocognitive disorder; 49 with cognitive normal findings) and from 283 normal controls. Cut-offs were identified based on (a) Youden's index and (b) the 10th percentile of the control group.
Results:
A cut-off of 23/24 on the MoCA had better correct classification rates than the MMSE and the original MoCA cut-off. Compared to the original MoCA cut-off, the cut-off of 23/24 points had higher specificity (92% vs 63%), but lower sensitivity (65% vs 86%). Introducing two separate cut-offs increased diagnostic accuracies with 92% specificity (23/24 points) and 91% sensitivity (26/27 points). Scores between these two cut-offs require further examinations.
Conclusions:
Using two separate cut-offs for the MoCA combined with scores in an indecisive area enhances the accuracy of cognitive screening.
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