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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The MemTrax Test Compared to the Montreal Cognitive Assessment Estimation of Mild Cognitive Impairment
Marjanne D van der Hoek1,2, Arie Nieuwenhuizen2, Jaap Keijer2
1Applied Research Centre Food and Dairy, Van Hall Larenstein University of Applied Sciences, Leeuwarden, the Netherlands.
Abstract:
Cognitive impairment is a leading cause of dysfunction in the elderly. When mild cognitive impairment (MCI) occurs in elderly, it is frequently a prodromal condition to dementia. The Montreal Cognitive Assessment (MoCA) is a commonly used tool to screen for MCI. However, this test requires a face-to-face administration and is composed of an assortment of questions whose responses are added together by the rater to provide a score whose precise meaning has been controversial. This study was designed to evaluate the performance of a computerized memory test (MemTrax), which is an adaptation of a continuous recognition task, with respect to the MoCA. Two outcome measures are generated from the MemTrax test: MemTraxspeed and MemTraxcorrect. Subjects were administered the MoCA and the MemTrax test. Based on the results of the MoCA, subjects were divided in two groups of cognitive status: normal cognition (n = 45) and MCI (n = 37). Mean MemTrax scores were significantly lower in the MCI than in the normal cognition group. All MemTrax outcome variables were positively associated with the MoCA. Two methods, computing the average MTX score and linear regression were used to estimate the cutoff values of the MemTrax test to detect MCI. These methods showed that for the outcome MemTraxspeed a score below the range of 0.87 - 91 s-1 is an indication of MCI, and for the outcome MemTraxcorrect a score below the range of 85 - 90% is an indication for MCI.
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