Feature Selection for Dementia Classification using Support Vector Machine
Abstract
Feature selection is of great importance in medical
image classification especially neuroimaging classification for
determining the most relevant features that will aid in accurate
diagnosis of neuropsychological diseases. This paper presents a
comparison of feature selection algorithms based on Support Vector
Machine (SVM). To achieve robust performance and optimal
selection of parameters involved in feature selection, and
classification, prior knowledge is embedded to generate multiple
versions of training and testing sets for parameter optimization. The
integrated feature extraction and selection method is applied to a
Structural Magnetic Resonance image based Alzheimer’s dementia
(AD) study with four different sets of non-demented and demented
subjects. Cross-validation results of our study clearly indicate that the
algorithm SVM-RFE trained with prior knowledge achieves 98%
accuracy with Radial Basis Function(RBF) kernel and can improve
performance of the classifier. This novel method of inculcating prior
knowledge in SVM-RFE method which is tested in 4 different sets of
datasets reveals that RBF kernel is found to outperform other kernels
with a mean sensitivity of 97%, and thereby aids in quick and
efficient classification of dementia.
Keywords
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