Hybrid feature selection method for autism spectrum disorder SNPs

Alzubi, Raid; Ramzan, Naeem; Alzoubi, Hadeel

doi:10.1109/cibcb.2017.8058526

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…The emphasis is on selecting the optimal features of autism and improving classification while maintaining high accuracy. Therefore, by reducing data dimensionality and choosing the appropriate and essential autism features, a machine learning algorithm will show promising results in diagnosing ASD [13,18,25,[52][53][54][55][56]. Nevertheless, several issues must be addressed, for instance, improper sampling methods, redundancy of features, imbalanced and insignificant data sizes, which influence accuracy.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Different studies have applied genomics data such as SNPs, spatiotemporal gene expression patterns, and common variants to predict ASD by using a machine-learning based approach [225][226][227]. Other studies have utilized data from neuroimaging techniques such as fMRI [228][229][230][231][232], sMRI [233] and PET [234] from ASD patients by implementing different ML tools to identify objective biomarkers, which is an important clinical goal in the management of ASD.…”

Section: Machine Learning To Detect Asd Biomarkersmentioning

confidence: 99%

Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder

Nisar

Haris

2023

Mol Psychiatry

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Autism-spectrum disorders (ASDs) are developmental disabilities that manifest in early childhood and are characterized by qualitative abnormalities in social behaviors, communication skills, and restrictive or repetitive behaviors. To explore the neurobiological mechanisms in ASD, extensive research has been done to identify potential diagnostic biomarkers through a neuroimaging genetics approach. Neuroimaging genetics helps to identify ASD-risk genes that contribute to structural and functional variations in brain circuitry and validate biological changes by elucidating the mechanisms and pathways that confer genetic risk. Integrating artificial intelligence models with neuroimaging data lays the groundwork for accurate diagnosis and facilitates the identification of early diagnostic biomarkers for ASD. This review discusses the significance of neuroimaging genetics approaches to gaining a better understanding of the perturbed neurochemical system and molecular pathways in ASD and how these approaches can detect structural, functional, and metabolic changes and lead to the discovery of novel biomarkers for the early diagnosis of ASD.

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“…Para isso eles mesmos selecionaram crianças, cujo diagnóstico do TEA já tivesse sido realizado, e aplicaram algoritmos para detalhar as características faciais de cada indivíduo criando, assim, uma BD. Após esse processo, os dados foram submetidos a três algoritmos de AM, sendo eles: SVM, RF e Neural Networks Multilayer Perceptron (MLP), descrevendo dois cenários distintos, onde o primeiro cenário foram empregadas [32] Genéticos SVM, Naive Bayes (NB), LDA, KNN Liu et al (2020) [33] Imagens ressônancia magnética SVM Elnakieb et al (2020) [34] Imagens ressônancia magnética SVM, DT, KNN, Neural Network (NN) Huang et al (2019) [35] Imagens ressônancia magnética SVM ElNakieb et al (2019) [36] Imagens ressônancia magnética SVM, KNN, DT, NN, Deep Neural Network (DNN) Mostafa, Tang and Wu (2019) [37] Imagens ressônancia magnética LDA, Logistic Regression (LR), SVM, KNN, NN Haputhanthri et al (2019) [38] Voz por EGG SVM, LR, RF, NB Wu et al (2021) [39] Imagens Vídeo SVM Sidhu (2019) [40] Imagens ressônancia magnética Principal Component Analysis (PCA), Independent Component Analysis (ICA), SVM Hasan, Jailani and Tahir (2018) [41] Imagens 3D movimentos KNN, SVM, NN Vijayalakshmi et al (2020) [42] Comportamentais demográficos NB, RF, LR Akter et al (2019) [43] Comportamentais biológicos SVM, DT, LR Roopa and Prasad (2019) [44] Imagens ressônancia magnética SVM, RF, DNN Huang, Liu and Tan (2020) [45] Imagens ressônancia magnética SVM Aslam et al (2021) [46] Imagens [48].…”

Section: Trabalhos Relacionadosunclassified

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Artoni

Barbosa

Morandini

2022

RITA

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Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios.

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Hybrid feature selection method for autism spectrum disorder SNPs

Cited by 15 publications

References 28 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

Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder

Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

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