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Updated: Nov 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A new computerized assessment battery for cognition (C-ABC) to detect mild cognitive impairment and dementia around 5
Moeko Noguchi-Shinohara1,2, Chiaki Domoto1, Taketoshi Yoshida3
1Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.
Abstract:
This study aimed to develop a new computerized assessment battery for cognition (C-ABC) to detect mild cognitive impairment (MCI) and dementia. We performed C-ABC in subjects with dementia (n = 422), MCI (n = 145), and normal cognition (NC; n = 574), and analyzed by age stratum (50s, 60s, and 70-85 years). To distinguish MCI from NC, the C-ABC total combined score, which were calculated by dividing the C-ABC total score by the C-ABC required time, revealed the best area under the curves (AUC) at 0.838 and 0.735 in the 50s and 60s age groups, respectively; notably, this entire procedure took approximately 5 min. To distinguish dementia from NC and MCI, the partial items of C-ABC (items 3 + 6 combined score) revealed the best AUCs at 0.910, 0.874, and 0.882 in the 50s, 60s, and 70-85 age groups, respectively. Furthermore, the items 3 + 6 combined score established the best AUC at 0.794 in the 70-85 age group to distinguish MCI from NC; this entire procedure took around 2 min. Hence, this study suggests that C-ABC could be a useful tool for detecting dementia or MCI in a short time.

