Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

Journal: Journal of neuroscience methods
Published Date: May 15, 2018

Abstract

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments.

Authors

  • J Ramírez
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain. Electronic address: javierrp@ugr.es.
  • J M Górriz
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
  • A Ortiz
    Dept. Communications Engineering, University of Málaga, Spain.
  • F J Martínez-Murcia
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain.
  • F Segovia
    1Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
  • D Salas-Gonzalez
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain.
  • D Castillo-Barnes
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain.
  • I A Illán
    Dept. of Signal Theory, Networking and Communications, University of Granada, Spain.
  • C G Puntonet
    Dept. Architecture and Computer Technology, University of Granada, Spain.

Keywords

External Resources

Popular Topics
Recent Journals