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

Journal: Journal of the International Neuropsychological Society : JINS
Published Date: Mar 23, 2020

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).

Authors

  • Russell Binaco
    Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA.
  • Nicholas Calzaretto
    Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA.
  • Jacob Epifano
    Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA.
  • Sean McGuire
    Signal Processing and Pattern Recognition Laboratory, Rowan University Glassboro, Glassboro, NJ, USA.
  • Muhammad Umer
    Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
  • Sheina Emrani
    Department of Psychology, Rowan University, Glassboro, NJ, USA.
  • Victor Wasserman
    Department of Psychology, Rowan University, Glassboro, NJ, USA.
  • David J Libon
    Drexel Neuroscience Institute, Drexel University College of Medicine, dlibon@drexelmed.edu.
  • Robi Polikar

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