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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A comparison of theoretical and statistically derived indices for predicting cognitive decline
Holly Wilhalme1, Naira Goukasian2, Fransia De Leon2
1Department of Medicine Statistics Core, University of California, Los Angeles, Los Angeles, CA, USA.
Introduction:
Both theoretical and statistically derived approaches have been used in research settings for predicting cognitive decline.
Methods:
Fifty-eight cognitively normal (NC) and 71 mild cognitive impairment (MCI) subjects completed a comprehensive cognitive battery for up to 5 years of follow-up. Composite indices of cognitive function were derived using a classic theoretical approach and exploratory factor analysis (EFA). Cognitive variables comprising each factor were averaged to form the EFA composite indices. Logistic regression was used to investigate whether these cognitive composites can reliably predict cognitive outcomes.
Results:
Neither method predicted decline in NC. The theoretical memory, executive, attention, and language composites and the EFA-derived "attention/executive" and "verbal memory" composites were significant predictors of decline in MCI. The best models achieved an area under the curve of 0.94 in MCI.
Conclusions:
The theoretical and the statistically derived cognitive composite approaches are useful in predicting decline in MCI but not in NC.
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