[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117395-en":3,"doc-seo-117395-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117395,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders","Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that can significantly affect daily functioning, with severity often reduced through timely early interventions. The framework evaluates multiple machine learning approaches for early ASD detection by applying four feature scaling strategies—Quantile Transformer, Power Transformer, Normalizer, and Max Abs Scaler—followed by eight common classifiers. Experiments on four standard ASD datasets (Toddlers, Adolescents, Children, Adults) compare results using accuracy, ROC analysis, F1-score, precision, recall, MCC, kappa, and log loss, then identify the best-performing classifier-feature scaling pair per dataset. Feature importance is further assessed via four feature selection techniques (Info Gain, Gain Ratio, Relief F, and Correlation), and risk factors are ranked to support healthcare screening decisions.","Received 13 December 2022, accepted 25 December 2022, date of publication 26 December 2022, date of current version 16 February 2023.  \nDigital Object Identifier 10.1109/ACCESS.2022.3232490  \nA Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders  \nS. M. MAHEDY HASAN 1, MD PALASH UDDIN2,3,(Member, IEEE), MD AL MAMUN 1,(Senior Member, IEEE), MUHAMMAD IMRAN SHARIF4, ANWAAR ULHAQ5, AND GOVIND KRISHNAMOORTHY6  \n1Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh  \n2Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh  \n3 School of Information Technology, Deakin University, Geelong, VIC 3220, Australia  \n4Department of Computer Science, COMSATS University Islamabad, Wah Campus, Punjab 47040, Pakistan  \n5 School of Computing, Mathematics and Engineering, Charles Sturt University, Port Macquarie, NSW 2444, Australia  \n6 School of Psychology and Wellbeing, University of Southern Queensland, Ipswich, QLD 4305, Australia Corresponding author: Anwaar Ulhaq ([aulhaq@csu.edu.au](aulhaq@csu.edu.au))  \nThis work was supported by the Regional Australia Mental Health Research and Training Institute, Manna Institute, NSW, Australia, under Grant 0000103935 .  \nABSTRACT Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS) . Then, the feature-scaled datasets are classi􀀜ed through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) . Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults) . Comparing the classi􀀜cation outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coef􀀜cient: MCC, Kappa score, and Log loss), the best-performing classi􀀜cation methods, and the best FS techniques for each ASD dataset are identi􀀜ed. After analyzing the experimental outcomes of different classi􀀜ers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE) . These detailed experimental evaluations indicate that proper 􀀜netuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.  \nINDEX TERMS Autism spectrum disorder, machine ","cbCaifUAGBtfCQwj","https://ap.wps.com/l/cbCaifUAGBtfCQwj","pdf",3837099,1,20,"English","en",105,"# I. Introduction\n## Background and challenges of ASD identification\n## Motivation for early intervention and ML-based screening\n# A. Proposed evaluation framework\n## Feature scaling strategies\n## Classifier models and performance metrics\n## Feature selection and risk-factor ranking","[{\"question\":\"What problem does the framework address in autism screening?\",\"answer\":\"It focuses on early-stage detection of Autism Spectrum Disorder (ASD) using machine learning, aiming to enable earlier interventions that can mitigate severity.\"},{\"question\":\"Which feature scaling strategies and classifiers are evaluated?\",\"answer\":\"Four feature scaling methods are used—Quantile Transformer, Power Transformer, Normalizer, and Max Abs Scaler—then the scaled data are classified using Ada Boost, Random Forest, Decision Tree, KNN, Gaussian Naïve Bayes, Logistic Regression, SVM, and Linear Discriminant Analysis.\"},{\"question\":\"How are important risk factors determined in the study?\",\"answer\":\"After identifying best-performing methods, ASD risk factors are computed and attribute importance is ranked using four feature selection techniques: Info Gain Attribute Evaluator, Gain Ratio Attribute Evaluator, Relief F Attribute Evaluator, and Correlation Attribute Evaluator.\"}]","A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders | 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problem does the framework address in autism screening?","Question",{"text":75,"@type":76},"It focuses on early-stage detection of Autism Spectrum Disorder (ASD) using machine learning, aiming to enable earlier interventions that can mitigate severity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which feature scaling strategies and classifiers are evaluated?",{"text":80,"@type":76},"Four feature scaling methods are used—Quantile Transformer, Power Transformer, Normalizer, and Max Abs Scaler—then the scaled data are classified using Ada Boost, Random Forest, Decision Tree, KNN, Gaussian Naïve Bayes, Logistic Regression, SVM, and Linear Discriminant Analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How are important risk factors determined in the study?",{"text":84,"@type":76},"After identifying best-performing methods, ASD risk factors are computed and attribute importance is ranked using four feature selection techniques: Info Gain Attribute Evaluator, Gain Ratio Attribute Evaluator, Relief F Attribute Evaluator, and Correlation Attribute Evaluator.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,117,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & 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