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Updated: Mar 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Informant questionnaire on cognitive decline in the elderly (IQCODE) for classifying cognitive dysfunction as
1Department of Neurology, Korea University Medical College, Seoul, South Korea & Korea University Ansan Hospital, Ansan, South Korea.
Background:
The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) is a reliable, validated informant-based instrument in screening for cognitive dysfunction. However, previous studies have evaluated only the ability to discriminate dichotomously, such as dementia from cognitively normal (CN) individuals or mild cognitive impairment (MCI) from CN. This study investigated the ability of the IQCODE to classify not only dichotomous but also multiple stages of cognitive dysfunction.
Methods:
We examined 228 consecutive participants (76 CN, 76 with MCI, and 76 with dementia). Receiver operating characteristic (ROC) curves determined dichotomous classification parameters. Multi-category ROC surfaces were evaluated to classify three stages of cognitive dysfunction.
Results:
Dichotomous classification using the ROC curve analyses showed that the area under the ROC curve was 0.91 for dementia from participants without dementia and 0.71 for MCI from CN. Simultaneous multi-category classification analyses showed that the volume under the ROC surface was 0.61 and the derived optimal cut-off points were 3.15 and 3.73 for CN, MCI, and dementia. The Youden index for the IQCODE was estimated as 0.51 and the derived optimal cut-off points were 3.33 and 3.70. The overall classification accuracy by the VUS was 58.3% and that by the Youden index 61.8%.
Conclusions:
IQCODE is useful to classify the dichotomous and multi-category stages of cognitive dysfunction.
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