Neuroinformatics

, Volume 14, Issue 3, pp 279–296 | Cite as

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia

  • Jussi Tohka
  • Elaheh Moradi
  • Heikki Huttunen
  • Alzheimer’s Disease Neuroimaging Initiative
Original Article

Abstract

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer’s disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Keywords

Magnetic Resonance Imaging Machine Learning Feature selection Alzheimer’s Disease Classification Multivariate pattern analysis 

Supplementary material

12021_2015_9292_MOESM1_ESM.pdf (167 kb)
(PDF 167 KB)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Bioengineering and Aerospace EngineeringUniversidad Carlos III de MadridLeganesSpain
  2. 2.Instituto de Investigación Sanitaria Gregorio MarañonMadridSpain
  3. 3.Department of Signal ProcessingTampere University of TechnologyTampereFinland

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