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Updated: Jun 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Optimizing the CAMCOG test in the screening for mild cognitive impairment and incipient dementia: saving time with
Ivan Aprahamian1, Breno Satler Diniz, Rafael Izbicki
1Psychogeriatric Clinic, Laboratory of Neuroscience (LIM 27), Department and Institute of Psychiatry, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil. ivan.aprahamian@terra.com.br
Objective:
To identify the CAMCOG sub-items that best contribute for the identification of patients with mild cognitive impairment (MCI) and incipient Alzheimer's disease (AD) in clinical practice.
Methods:
Cross-sectional assessment of 272 older adults (98 MCI, 82 AD, and 92 controls) with a standardized neuropsychological battery and the CAMCOG schedule. Backward logistic regression analysis with diagnosis (MCI and controls) as dependent variable and the sub-items of the CAMCOG as independent variable was carried out to determine the CAMCOG sub-items that predicted the diagnosis of MCI.
Results:
Lower scores on Language, Memory, Praxis, and Calculation CAMCOG sub-items were significantly associated with the diagnosis of MCI. A composite score obtained by the sum of these scores significantly discriminated MCI patients from comparison groups. This reduced version of the CAMCOG showed similar diagnostic accuracy than the original schedule for the identification of patients with MCI as compared to controls (AUC = 0.80 ± 0.03 for the reduced CAMCOG; AUC = 0.79 ± 0.03 for the original CAMCOG).
Conclusion:
This reduced version of the CAMCOG had similar diagnostic properties as the original CAMCOG and was faster and easier to administer, rendering it more suitable for the screening of subtle cognitive deficits in general clinical practice.
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