ArticleFrontiers in genetics2018
Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- A Dual-Stream Transformer with Self-Supervised Contrastive Training for fMRI-Based Autism Spectrum Disorder Classification.Brain sciences · 2026Article
- Common and distinct changes of regional resting-state brain activity between high- and low-functioning autism spectrum disorder.BMC psychiatry · 2026Article
- Automatic diagnosis of autism spectrum disorders in children through resting-state functional magnetic resonance imaging with machine vision.Quantitative imaging in medicine and surgery · 2025Article
- The changes in brain network functional gradients and dynamic functional connectivity in SeLECTS patients revealing disruptive and compensatory mechanisms in brain networks.Frontiers in psychiatry · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- The alterations in brain network functional gradients and dynamic functional connectivity in Alzheimer's disease: a resting-state fMRI study.Frontiers in aging neuroscience · 2025Article
- Article
- Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder.European child & adolescent psychiatry · 2024Article
- A novel approach for ASD recognition based on graph attention networks.Frontiers in computational neuroscience · 2024Article
- Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.NeuroImage. Clinical · 2024Article
- Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines.Lasers in medical science · 2023Article
- Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.Medical & biological engineering & computing · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children.Frontiers in psychiatry · 2022Review
- A Multiview Deep Learning Method for Brain Functional Connectivity Classification.Computational intelligence and neuroscience · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning.PloS one · 2022Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.Brain and behavior · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM) are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC). Resting-state functional magnetic resonance imaging (fMRI) data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG) (orbital and opercula part), hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.
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