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Updated: Jun 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Combined memory and executive function tests can screen mild cognitive impairment and converters to dementia in a
Eriko Nakata1, Mari Kasai, Masashi Kasuya
1Department of Geriatric Behavioral Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Background:
The borderline condition between health and dementia, defined as Clinical Dementia Rating (CDR) 0.5, should be detected for the possible prediction of dementia. Since the CDR requires information from collateral sources, it is difficult to rate people living alone. The aim is to develop a set of tests without collateral information for detecting CDR 0.5 and converters to dementia.
Methods:
625 participants were selected from the community; 412 were CDR 0 (healthy), 168 were CDR 0.5 (defined here as mild cognitive impairment; MCI), and were 45 CDR 1+ (dementia). Neuropsychological tests were administered to assess memory, orientation, attention and executive function. We analyzed various combinations of tests by receiver operating characteristic curve and area under the curve (AUC). Among the participants, 497 were randomly selected to be re-examined after 5 years to predict further decline towards dementia.
Results:
We found that a combination of tests for orientation, memory, attention, executive function, and abstraction and judgment could discriminate subjects with MCI from healthy participants with high accuracy (AUC = 0.83). The predictive accuracy was better than that of the Mini Mental State Examination (AUC = 0.77). The same tests, except orientation, could also predict converters to dementia (AUC = 0.88).
Conclusions:
We consider that a combination of tests can be helpful for the early detection of individuals with MCI and converters to dementia in the community.
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