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Updated: Mar 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Improved predictive ability of the Montreal Cognitive Assessment for diagnosing dementia in a community-based study
Jung-Lung Hsu1,2,3, Yen-Chun Fan4, Ya-Li Huang5,6
1Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan. tulu@ms36.hinet.net.
Introduction:
We compared the predictive ability of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) to diagnose dementia in a community-based study.
Methods:
A total of 276 people aged 60 years or older were enrolled. All of the participants were administered face-to-face interview questionnaires and MoCA and MMSE examinations. The receiver operating characteristic curve method and area under curve were performed to assess the predictive ability for diagnosing dementia.
Results:
The 276 participants had a mean age of 67.9 ± 6.1 years and mean education duration of 11.4 ± 4.0 years. In general, the MoCA yielded higher AUCs (0.891) with favorable sensitivity (78 %) and excellent specificity (94 %) compared with the MMSE in differentiating the participants with and without dementia in either the total sample or all subgroups.
Conclusion:
Our study determined a higher predictive ability in the MoCA than in the MMSE for diagnosing dementia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria in a community-based sample with a broader range of education level.
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