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Updated: Oct 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Adjusting the Thai version of the Addenbrooke's Cognitive Examination III for education to screen for dementia
1Department of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Pathumthani, Thailand.
Abstract:
Aim: To examine whether education adjusted cut-off points of the Thai version of the ACE-III improve diagnostic accuracy in the detection of mild cognitive impairment (MCI) and dementia. Materials & methods: There were 172 participants consisting of 70 normal controls, 49 people with MCI and 53 patients with dementia. Results: To screen for MCI, the adjusted for education method yielded greater accuracy for the area under the receiver operating characteristic curve (AuROC) than the unadjusted method (0.9-0.92 vs 0.86). For the detection of dementia, when applying the education correction, AuROC increased from 0.87 (unadjusted) to 0.91 for the education >6 group, but there was no improvement for education ≤6 group (AuROC 0.86). Conclusion: The use of adjusted cut-off score for education level could increase the diagnostic accuracy of the test.
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