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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Screening for Normal Cognition, Mild Cognitive Impairment, and Dementia with the Korean Dementia Screening
Sun-Ju Lee1, Jung-Hoon Han1, Jung-Won Hwang1
1Department of Neurology, Korea University Ansan Hospital, Ansan, Republic of Korea.
Objective:
The Korean Dementia Screening Questionnaire (KDSQ) is an informant-based instrument used to screen for cognitive dysfunction. However, its ability to only dichotomously discriminate between dementia and normal cognition has been previously investigated. This study investigated the ability of the KDSQ to classify not only dichotomous but also multiple stages of cognitive dysfunction.
Methods:
We examined 582 participants. Receiver operating characteristic (ROC) curves were used to determine dichotomous classification parameters. Multi-category ROC surfaces were evaluated to classify the three stages of cognitive dysfunction.
Results:
Dichotomous classification using the ROC curve analyses showed that the area under the curve was 0.92 for dementia for subjects without dementia and 0.96 for dementia in controls. Simultaneous multi-category classification analyses showed that the volume under the ROC surface (VUS) was 0.57 and that the derived optimal cut-off points were 2 and 8 for controls, MCI, and dementia. The estimated Youden index for the KDSQ was 0.48, and the derived optimal cut-off points were 5 and 10. The overall classification accuracy of the VUS and Youden index was 61.2% and 58.6%, respectively.
Conclusion:
The KDSQ is useful for classifying dichotomous and multi-category stages of cognitive dysfunction.
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