Analysis of Asperger syndrome using genetic-evolutionary random support vector machine cluster

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ID: 25389
2018
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Ranked #29 of 73 articles by views in Frontiers in physiology

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Abstract
Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in ABIDE database. The classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS, and a universal framework for other brain science research as the model has excellent generalization performance.
Reference Key
bi2018analysisfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bi, X.
Journal Frontiers in physiology
Year 2018
DOI
10.3389/fphys.2018.01646
URL
https://www.frontiersin.org/article/10.3389/fphys.2018.01646/full
Keywords Keywords not found

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