Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster
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 classi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-11-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2018.01646/full |
| _version_ | 1811215480361320448 |
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| author | Xia-an Bi Jie Chen Qi Sun Yingchao Liu Yang Wang Xianhao Luo |
| author_facet | Xia-an Bi Jie Chen Qi Sun Yingchao Liu Yang Wang Xianhao Luo |
| author_sort | Xia-an Bi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T06:23:19Z |
| format | Article |
| id | doaj.art-0bd17522203c4714a54a007b0b50eb14 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-12T06:23:19Z |
| publishDate | 2018-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-0bd17522203c4714a54a007b0b50eb142022-12-22T03:44:13ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-11-01910.3389/fphys.2018.01646417380Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine ClusterXia-an BiJie ChenQi SunYingchao LiuYang WangXianhao LuoAsperger 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.https://www.frontiersin.org/article/10.3389/fphys.2018.01646/fullgenetic-evolutionary random SVM clusterfunctional connectivityclassificationAsperger syndromeabnormal brain regions |
| spellingShingle | Xia-an Bi Jie Chen Qi Sun Yingchao Liu Yang Wang Xianhao Luo Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster Frontiers in Physiology genetic-evolutionary random SVM cluster functional connectivity classification Asperger syndrome abnormal brain regions |
| title | Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster |
| title_full | Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster |
| title_fullStr | Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster |
| title_full_unstemmed | Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster |
| title_short | Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster |
| title_sort | analysis of asperger syndrome using genetic evolutionary random support vector machine cluster |
| topic | genetic-evolutionary random SVM cluster functional connectivity classification Asperger syndrome abnormal brain regions |
| url | https://www.frontiersin.org/article/10.3389/fphys.2018.01646/full |
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