Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning
Daehyuk Yim, Tae Young Yeo, Moon Ho Park
Abstract
OBJECTIVE: To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. METHODS: This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant's caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen's kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. RESULTS: The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. CONCLUSION: This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.