Neuropsychological test selection for cognitive impairment classification: A machine learning approach

Journal of Clinical and Experimental Neuropsychology
Alyssa WeakleyDiane J Cook

Abstract

Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI), or dementia using a suite of classification techniques. Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis; Clinical Dementia Rating, CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals with CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. A total of 27 demographic, psychological, and neuropsychological variables were available for variable selection. No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0-99.1%), geometric mean (60.9-98.1%), sensitivity (44.2-100%), and specificity (52.7-1...Continue Reading

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Citations

Feb 3, 2016·Journal of Alzheimer's Disease : JAD·Yudong ZhangTi-Fei Yuan
Nov 24, 2017·Scientific Reports·Silvia SerinoGiuseppe Riva
Feb 23, 2020·BMC Medical Informatics and Decision Making·Harsh BhasinUNKNOWN For Alzheimer’s Disease Neuroimaging Initiative
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Mar 30, 2021·Journal of Alzheimer's Disease : JAD·Michael J KleimanUNKNOWN Alzheimer’s Disease Neuroimaging Initiative

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Software Mentioned

Statistical Package for Social Sciences
Orange
SPSS

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