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Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer’s Disease

Published online by Cambridge University Press:  23 March 2020

Russell Binaco ,
Nicholas Calzaretto ,
Jacob Epifano ,
Sean McGuire ,
Muhammad Umer ,
Sheina Emrani Open the ORCID record for Sheina Emrani [Opens in a new window] ,
Victor Wasserman Open the ORCID record for Victor Wasserman [Opens in a new window] ,
David J. Libon  and
Robi Polikar Open the ORCID record for Robi Polikar [Opens in a new window]
Russell Binaco
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
Nicholas Calzaretto
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
Jacob Epifano
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
Sean McGuire
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
Muhammad Umer
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
Sheina Emrani
Affiliation:
Department of Psychology, Rowan University, Glassboro, NJ, USA
Victor Wasserman
Affiliation:
Department of Psychology, Rowan University, Glassboro, NJ, USA
David J. Libon
Affiliation:
Department of Psychology, Rowan University, Glassboro, NJ, USA New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Stratford, NJ, USA
Robi Polikar*
Affiliation:
Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA
*
*Correspondence and reprint requests to: Robi Polikar, PhD, Signal Processing and Pattern Recognition Laboratory, Rowan University, Glassboro, NJ, USA. E-mail: polikar@rowan.edu
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Abstract

Objective:

To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).

Methods:

dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.

Results:

Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.

Conclusion:

Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

Keywords

Clock drawingThe Digital Clock Drawing TestMild cognitive impairmentMachine learningCognitive assessment
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 7 , August 2020 , pp. 690 - 700
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

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Footnotes

†

These authors contributed equally to the work.

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