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Updated: Aug 10, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Usefulness of the Cognitive Composition Test as an Early Discriminator of Mild Cognitive Impairment
Yoshiki Tamaru1, Hiroyuki Sumino2, Akiyoshi Matsugi1
1Faculty of Rehabilitation, Shijonawate-Gakuen University, Hojo 5-11-10, Daito 574-0011, Osaka, Japan.
Abstract:
Mild cognitive impairment (MCI) is the preliminary stage of dementia, which is a serious social problem worldwide. This study aimed to investigate whether the Cognitive Composition Test (CCT) is effective for the early diagnosis of MCI. A total of 104 older adults underwent the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), the Trail Making Test Parts A (TMT-A) and B (TMT-B), and our newly prototyped cognitive composition test (CCT). We created three types of CCT (CCT-A, CCT-B, and CCT-C) with different degrees of difficulty. First, we examined the concurrent validity of CCT-A, CCT-B, and CCT-C with the MoCA, MMSE, TMT-A, and TMT-B. All participants were classified into the healthy control (HC) and MCI groups based on their scores in the Japanese versions of the MoCA and MMSE. The HC and MCI groups were compared using the TMT-A, TMT-B, CCT-A, CCT-B, and CCT-C. Finally, we examined the sensitivity for discrimination of CCT-C. CCT-C had a higher discrimination sensitivity than TMT-A, TMT-B, CCT-A, and CCT-B, with a cut-off value of 65.75 s, a sensitivity level of 0.844, and a specificity of 0.776. It may be a useful screening tool for the early diagnosis of the early-stages of dementia, such as MCI, in asymptomatic older adults.
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