Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.

Journal: Computers in biology and medicine
PMID: 31284154

Abstract

BACKGROUND: Given the increasing recognition of the significance of non-motor symptoms in Parkinson's disease, we investigate the optimal use of machine learning methods for the prediction of the Montreal Cognitive Assessment (MoCA) score at year 4 from longitudinal data obtained at years 0 and 1.

Authors

  • Mohammad R Salmanpour
    Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
  • Mojtaba Shamsaei
    Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
  • Abdollah Saberi
    Department of Computer Engineering, Islamic Azad University, Tehran, Iran.
  • Saeed Setayeshi
    Institute for Cognitive Sciences Studies, Tehran, Iran; Medical Radiation Eng. Department, Faculty of Physics and Energy Eng., Amirkabir University of Technology, (Tehran Polytechnics), Tehran, Iran. Electronic address: setayesh@aut.ac.ir.
  • Ivan S Klyuzhin
    Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
  • Vesna Sossi
    Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada.
  • Arman Rahmim

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