[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120390-en":3,"doc-seo-120390-105":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":20,"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},120390,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Autism spectrum disorder classification using machine learning with factor analysis - read online free","Autism spectrum disorder (ASD) is complex and heterogeneous, making diagnosis and categorization difficult to standardize, especially across early childhood. This study proposes an integrated approach combining machine learning with factor analysis and correlation validation to improve robustness of ASD classification for toddlers. Factor analysis is used to uncover latent variables behind ASD features and reduce the original high-dimensional feature space. Machine-learning classifiers then use the extracted components, while Pearson correlation assesses associations between latent components and diagnostic labels, supporting improved diagnostic precision.","IAES International Journal of Artificial Intelligence (IJ-AI)  \nVol. 14, No. 3, June 2025, pp. 2185∼2195  \nISSN: 2252-8938, DOI: 10.11591/ijai.v14.i3.pp2185-2195 ❒ 2185  \n\n| Autism spectrum disorder classification using machine learning with factor analysis\u003Cbr>Disha Devidas Nayak1,2 , Seema Shedole1,3 , Archana Mathur4\u003Cbr>1Visvesveraya Technological University, Belagavi, India\u003Cbr>2Department of Artificial Intelligence and Machine Learning, NMAM Institute of Technology, Nitte Deemed to be University, Udupi, India\u003Cbr>3Department of Computer Science in Ramaiah Institute of Technology, Bangalore, India\u003Cbr>4Department of Artificial Intelligence and Data Science, Nitte Meenakshi Institute of Technology, Bangalore, India |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Nov 21, 2023 Revised Feb 5, 2025 Accepted Mar 15, 2025\u003Cbr>Keywords:\u003Cbr>Autism spectrum disorder Correlation analysis Factor analysis Machine learning Pearson correlation |  | ABSTRACT\u003Cbr>Due to the complexity and heterogeneity of autism spectrum disorder (ASD), diagnosis and categorization have attracted a lot of interest. To improve the robustness of ASD classification across the toddler age group, this work proposes an integrated strategy that integrates machine learning approaches with factor analysis and correlation validation. Benchmark dataset representing toddlers used to test this strategy’s efficiency. To first find the latent variables behind the ASD features in each dataset, factor analysis is used. We intend to capture the shared variance between variables and lower the dimensionality of the initial feature space by identifying these latent components. The subsequent machine-learning classification models used the retrieved components as input features. To validate the categorization results, correlation analyses were carried out in addition to factor analysis. The associations between the latent components discovered by factor analysis and the diagnostic labels were examined using Pearson correlation, a measure of linear association. The results highlight the method’s potential to improve diagnostic precision and shed light on the intricate connections between characteristics and diagnostic labels on the autism spectrum for toddlers.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Disha Devidas Nayak\u003Cbr>Department of Artificial Intelligence and Machine Learning, NMAM Institute of Technology Nitte Deemed to be University\u003Cbr>Nitte, India\u003Cbr>Email: [disha.dn.2@gmail.com](disha.dn.2@gmail.com) |  |  |\n\n1. INTRODUCTION  \nOwing to the complex and varied character of the illness, the diagnosis and classification of autism spectrum disorder (ASD) have attracted a great deal of attention. ASD exhibits a wide range of symptoms and changes, making it difficult to classify the disorder accurately and consistently across age groups [1],[2] . ASD poses a formidable challenge in its diagnosis and classification owing to its intricate and heterogeneous nature. The condition encompasses a broad array of symptoms spanning social interaction, communication, behavior, and sensory processing domains, thereby complicating efforts to accurately and consistently categorize it across age cohorts [1] . ASD is typified by deficits in social reciprocity, manifesting as difficulties in discerning social cues, maintaining eye contact, interpreting facial expressions, and cultivating interpersonal relationships. Afflicted individuals often exhibit a propensity towards solitary pursuits, alongside a notable impediment in  \nsharing emotions or interests with others [2] . Communication impairments in ASD range from delayed language acquisition to outright verbal autism. Such behaviors often serve as mechanisms for self-regulation or sensory modulation. These deficits significantly impact academic performance, adaptive functioning, and autonomy in daily activities [3] . While some individuals may exhibit amelioratio","cbCaittj61PbdTSj","https://ap.wps.com/l/cbCaittj61PbdTSj","pdf",548759,1,11,"English","en",105,"# Introduction\n## Problem motivation and challenges\n## Proposed integrated methodology\n## Factor analysis and correlation validation","[{\"question\":\"Why is ASD classification difficult across age groups, especially for toddlers?\",\"answer\":\"ASD shows a wide range of symptoms and heterogeneity that complicates consistent categorization across age cohorts. High-dimensional, noisy datasets also make reliable feature selection challenging.\"},{\"question\":\"How does the method extract information from ASD datasets?\",\"answer\":\"Factor analysis is used first to identify latent variables behind observed ASD features and capture shared variance, reducing dimensionality before classification.\"},{\"question\":\"How are classification results validated in the study?\",\"answer\":\"Correlation analyses validate the categorization, using Pearson correlation to examine linear associations between the latent components discovered by factor analysis and diagnostic labels.\"}]","Autism spectrum disorder classification using machine learning with factor analysis - read online free | PDF",1785729787,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"autism-spectrum-disorder-classification-using-machine-learning-with-factor-analysis-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/autism-spectrum-disorder-classification-using-machine-learning-with-factor-analysis-read-online-free/120390/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is ASD classification difficult across age groups, especially for toddlers?","Question",{"text":75,"@type":76},"ASD shows a wide range of symptoms and heterogeneity that complicates consistent categorization across age cohorts. High-dimensional, noisy datasets also make reliable feature selection challenging.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method extract information from ASD datasets?",{"text":80,"@type":76},"Factor analysis is used first to identify latent variables behind observed ASD features and capture shared variance, reducing dimensionality before classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How are classification results validated in the study?",{"text":84,"@type":76},"Correlation analyses validate the categorization, using Pearson correlation to examine linear associations between the latent components discovered by factor analysis and diagnostic labels.","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},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]