Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data

Wang, Canhua; Xiao, Zhiyong; Wu, Jianhua

doi:10.1016/j.ejmp.2019.08.010

Cited by 93 publications

(48 citation statements)

References 21 publications

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“…In [19,26,48], the authors explained how to select the minimum features and classifications of ASD. The study by Hameed et al [48] discussed gene expression in ASD.…”

Section: Minimizing the Features Thatmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

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“…In [19,26,48], the authors explained how to select the minimum features and classifications of ASD. The study by Hameed et al [48] discussed gene expression in ASD.…”

Section: Minimizing the Features Thatmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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“…Chen et al [39] 66 Adora et al [40] 70-81 Anibal et al [41] 70 Nicha et al [42] 70.1 Our method 86.7…”

Section: Accuracy (%)mentioning

confidence: 90%

“…In this study, accuracy [40], sensitivity [40] and specificity [40] were used in the 5-fold cross-validation process to evaluation the model performance. The formulas used to calculate these three indicators are as follows:…”

Section: Classification Methods and Evaluation Of Performancementioning

confidence: 99%

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Shi

Zhang

2020

Symmetry

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder originating in infancy and childhood that may cause language barriers and social difficulties. However, in the diagnosis of ASD, the current machine learning methods still face many challenges in determining the location of biomarkers. Here, we proposed a novel feature selection method based on the minimum spanning tree (MST) to seek neuromarkers for ASD. First, we constructed an undirected graph with nodes of candidate features. At the same time, a weight calculation method considering both feature redundancy and discriminant ability was introduced. Second, we utilized the Prim algorithm to construct the MST from the initial graph structure. Third, the sum of the edge weights of all connected nodes was sorted for each node in the MST. Then, N features corresponding to the nodes with the first N smallest sum were selected as classification features. Finally, the support vector machine (SVM) algorithm was used to evaluate the discriminant performance of the aforementioned feature selection method. Comparative experiments results show that our proposed method has improved the ASD classification performance, i.e., the accuracy, sensitivity, and specificity were 86.7%, 87.5%, and 85.7%, respectively.

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“…Authors showed that alterations in the time series signal can be decomposed into the prevalent classes of change that are more useful in the subsequent analysis done using connectivity matrices. Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) was proposed by [ 38 ], where the correlation-based connectivity matrix was recursively pruned for discriminating features using SVM classifier, resulting in 90% accuracy on the dataset combined from all sites. Eigen features corresponding to 256 brain regions using the Laplacian matrix were proposed in [ 39 ] with the accuracy of 77%.…”

Section: Related Research Workmentioning

confidence: 99%

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

et al. 2020

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Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

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Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data

Cited by 93 publications

References 21 publications

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

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