Highlights
- •The proposed method improves the identification of autism on resting-state functional magnetic imaging data.
- •The most discriminative feature subset about functional connectivity in the brain of autism was found.
- •The classification accuracy obtained is better than the recent similar studies.
- •The results can be used as an important reference for autism diagnoses.
Abstract
Considering the unsatisfactory classification accuracy of autism due to unsuitable
features selected in current studies, a functional connectivity (FC)-based algorithm
for classifying autism and control using support vector machine-recursive feature
elimination (SVM-RFE) is proposed in this paper. The goal is to find the optimal features
based on FC and improve the classification accuracy on a large sample of data. We
chose 35 regions of interest based on the social motivation hypothesis to construct
the FC matrix and searched for informative features in the complex high-dimensional
FC dataset by the SVM-RFE with a stratified-4-fold cross-validation strategy. The
selected features were then entered into an SVM with a Gaussian kernel for classification.
A total of 255 subjects with autism and 276 subjects with typical development from
10 sites were involved in the study. For the data of global sites, the proposed classification
algorithm could identify the two groups with an accuracy of 90.60% (sensitivity 90.62%,
specificity 90.58%). For the leave-one-site-out test, the proposed algorithm achieved
a classification accuracy of 75.00%–95.23% for data from different sites. These promising
results demonstrate that the proposed classification algorithm performs better than
those in recent similar studies in that the importance of features can be measured
accurately and only the most discriminative feature subset is selected.
Keywords
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Article info
Publication history
Published online: August 22, 2019
Accepted:
August 13,
2019
Received in revised form:
August 8,
2019
Received:
April 27,
2019
Identification
Copyright
© 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
