[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126351-en":3,"doc-seo-126351-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126351,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Autism Spectrum Disorder Prediction in Children Using Machine Learning - A Study with Public Datasets","Life symptoms linked to autism spectrum disorder (ASD) begin in childhood and can continue through adolescence and adulthood. Genetic and environmental factors contribute to ASD, and early detection and treatment can improve long-term outcomes. Standard clinical testing remains costly and time intensive, motivating data-driven screening approaches. This study evaluates multiple machine-learning models, including SVM, random forest, naïve Bayes, logistic regression, K-nearest neighbor, and decision trees, using publicly available nonclinical ASD datasets. Logistic regression achieves the highest accuracy on the selected dataset.","Journal of Disability Research  \n2024 | Volume 3 | Pages: 1–9 | e-location ID: e20230064  \nDOI: 10.57197/JDR-2023-0064  \nAutism Spectrum Disorder Prediction in Children Using Machine Learning  \nMahmoud M. Abdelwahab1,2 ,*, Khamis A. Al-Karawi3,4 , E. M. Hasanin5 and H. E. Semary1,6  \n1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia  \n2Department of Basic Sciences, Higher Institute of Administrative Sciences, Osim, Egypt  \n3Department of Acoustic, School of Science, Engineering, and Environment, Salford University, Great Manchester, UK  \n4Department of Computer Science, Faculty of Science, Diyala University, Baqubah, Diyala, Iraq  \n5Faculty of Business Administration, Egyptian E-Learning University, Giza, Egypt  \n6Department of Statistics and Insurance, Faculty of Commerce, Zagazig University, Zagazig, Egypt  \nCorrespondence to:  \nMahmoud M. Abdelwahab*, [e-mail:](e-mail: mmabdelwahab@imamu.edu.sa)[ mmabdelwahab@imamu.edu.sa](e-mail: mmabdelwahab@imamu.edu.sa), Tel.: +966541065376  \nKhamis A. Al-Karawi, e-mail: [k.a.yousif@edu.salford.ac.uk](k.a.yousif@edu.salford.ac.uk)  \nE. M. Hasanin, e-mail: [Ihasanin@eelu.edu.eg](Ihasanin@eelu.edu.eg)  \nH. E. Semary, e-mail: [hesemary@imamu.edu.sa](hesemary@imamu.edu.sa)  \nReceived: August 31 2023; Revised: December 11 2023; Accepted: December 11 2023; Published Online: January 5 2024  \nABSTRACT  \nLife symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.  \nKEYWORDS  \nautism, machine learning, random forest, SVM, decision tree  \nINTRODUCTION  \nAutism spectrum disorder (ASD) is a neurodevelopmental condition affecting a child’s communication, social interaction, and knowledge acquisition, typically presenting within the first 2 years of life (Frith and Happé, 2005) . People with autism face various obstacles, including difficulty with focus, learning disabilities, mental health issues such as anxiety, depression, movement, sensory issues, and other challenges (Tripathy et al., 2021) . As a result, it impacts an individual’s entire cognitive, social, emotional, and physical health (Omar et al., 2019; Alenizi and Al-Karawi, 2023a) . The symptoms of this condition vary in extent and intensity, including communication difficulties, obsessive hobbies, and repeated mannerisms in social situations. A comprehensive examination is needed to detect ASD. This also comprises a thorough evalu","cbCaiu9iSnGScwPP","https://ap.wps.com/l/cbCaiu9iSnGScwPP","pdf",2089955,5,1,9,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is early detection of autism spectrum disorder (ASD) important?\",\"answer\":\"Early detection supports timely intervention, helps mitigate ASD symptoms, and improves long-term prospects. It can also reduce costs associated with delayed diagnosis.\"},{\"question\":\"What diagnostic challenge motivates using machine learning for ASD?\",\"answer\":\"Standard clinical tests for ASD are time consuming and expensive. Machine-learning approaches aim to improve efficiency and accuracy for screening and prediction.\"},{\"question\":\"Which machine-learning models are evaluated in the study?\",\"answer\":\"The study tests support vector machines, random forest classifier, naïve Bayes, logistic regression, K-nearest neighbor, and decision tree classification models.\"}]","Autism Spectrum Disorder Prediction in Children Using Machine Learning - A Study with Public Datasets | PDF",1785904615,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"autism-spectrum-disorder-prediction-in-children-using-machine-learning-a-study-with-public-datasets","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/autism-spectrum-disorder-prediction-in-children-using-machine-learning-a-study-with-public-datasets/126351/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is early detection of autism spectrum disorder (ASD) important?","Question",{"text":77,"@type":78},"Early detection supports timely intervention, helps mitigate ASD symptoms, and improves long-term prospects. It can also reduce costs associated with delayed diagnosis.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What diagnostic challenge motivates using machine learning for ASD?",{"text":82,"@type":78},"Standard clinical tests for ASD are time consuming and expensive. 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