Augmentative Semi-Supervised Learning for Autism Screening: A Novel Framework

Augmentative Semi-Supervised Learning for Autism Screening: A Novel Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Augmentative Semi-Supervised Learning for Autism Screening: A Novel Framework Rabia Naseer Rao, Hiran Thabrew, Seyed Reza Shahamiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8600100/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition for which early identification is essential to provide appropriate support and effective treatment. However, current diagnostic methods are resource-intensive and often inaccessible. Artificial Intelligence offers a promising alternative, but its effectiveness is hindered by algorithmic bias arising from data scarcity and imbalanced, largely unlabeled datasets. Such bias can lead to model overfitting, impaired learning, and poor generalization. While semi-supervised learning (SSL) can reduce reliance on manual labels through pseudo-label generation, conventional SSL approaches perform poorly under severe class imbalance, often amplifying label noise and bias. To address these challenges, we propose a novel Augmentative Semi-supervised Learning (ASSL) framework designed for robust learning in the presence of class imbalance and label scarcity. ASSL first applies pattern-based sampling to construct a balanced labeled dataset. It then employs a Collaborative Decision Labeling (CDL) strategy, where two heterogeneous models assign pseudo-labels using Dynamic Dual Thresholding (DDT), retaining only samples jointly and confidently labeled by both models. The framework was applied to the Autism AI dataset (over 12,000 participants), most of whom lacked diagnostic labels, producing severe class imbalance. ASSL improved sensitivity by 15.3%, specificity by 30.2%, and accuracy by 15.9% over conventional screening methods. Next, in external validation on the NHANES diabetes dataset, ASSL achieved a 7.9% gain in sensitivity and better discriminatory performance under imbalance. These results demonstrate that ASSL is a scalable and generalizable approach for limited and imbalanced health data tasks, offering a pathway to reduce algorithmic bias across screening applications. Autism Diagnosis Data Imbalance Semi-Supervised Learning Dynamic Dual Threshold Supervised Learning Autism AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8600100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585211283,"identity":"fe9d3e90-b70e-45d9-825a-18eea61fc51d","order_by":0,"name":"Rabia Naseer Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYJAC5h8VNXIgmuEBAwNjAwgR1MJw5pgxmE4gWgtjG3NiA0ILAWA++/DDxwVn2NL7ZyQ/NkiouSO74XZzA8OPGoY8eRyaZc6lGRvPqJDJnXEjzTgh4dgz4w13DjYw9hxjKMZlnwQPg5kEzxm23IYbOcwHEtgOJ264AXQkbwNDYjMOh0nwsH+T4G1jTpcHa/kH0cL4F6ilDacWHjNpoJYEA6CWhMQ2iBZmkC09uLUUG844c8xw45lnxgaJfYeNZwK1HJY5JpE4A7fDNj74UFEjL3c8+bHEh2+HZftupD98+KbGJnE+Du9jBweAZpGifhSMglEwCkYBGgAAT9JgyHdFPigAAAAASUVORK5CYII=","orcid":"","institution":"University of Auckland","correspondingAuthor":true,"prefix":"","firstName":"Rabia","middleName":"Naseer","lastName":"Rao","suffix":""},{"id":585211284,"identity":"efa9468c-e844-4325-81fa-fbc62c63c89f","order_by":1,"name":"Hiran Thabrew","email":"","orcid":"","institution":"University of Auckland","correspondingAuthor":false,"prefix":"","firstName":"Hiran","middleName":"","lastName":"Thabrew","suffix":""},{"id":585211285,"identity":"1962544e-6bbc-47fa-a2fe-c98902409633","order_by":2,"name":"Seyed Reza Shahamiri","email":"","orcid":"","institution":"University of Auckland","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Reza","lastName":"Shahamiri","suffix":""}],"badges":[],"createdAt":"2026-01-14 09:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8600100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8600100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101869105,"identity":"4ddffef9-44ac-4338-8d5b-86e4b6912134","added_by":"auto","created_at":"2026-02-04 12:58:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1005318,"visible":true,"origin":"","legend":"","description":"","filename":"ASSLJournalcogcompfinalwithauthors.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8600100/v1_covered_01644610-5978-4d99-8c1d-7233e1c82800.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Augmentative Semi-Supervised Learning for Autism Screening: A Novel Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autism Diagnosis, Data Imbalance, Semi-Supervised Learning, Dynamic Dual Threshold, Supervised Learning, Autism AI","lastPublishedDoi":"10.21203/rs.3.rs-8600100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8600100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutism Spectrum Disorder (ASD) is a neurodevelopmental condition for which early identification is essential to provide appropriate support and effective treatment. 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