Machine Learning Techniques and Chi-square Feature Selection for Diagnostic Classification Model of Autism Spectrum Disorder Using fMRI Data

Machine Learning Techniques and Chi-square Feature Selection for Diagnostic Classification Model of Autism Spectrum Disorder Using fMRI Data | 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 Machine Learning Techniques and Chi-square Feature Selection for Diagnostic Classification Model of Autism Spectrum Disorder Using fMRI Data Hossein Haghighat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7132626/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 Background: Autism Spectrum Disorder (ASD) diagnosis relies on subjective observation, hindered by ASD's diverse presentation, symptom overlap, and sex-specific neurobiology, causing misdiagnosis, especially in females. Thus, objective and reliable diagnostic methods are critical. New method: This rs-fMRI study built a sex-dependent ASD diagnostic classification model (DCM) using functional connectivity. After preprocessing, GICA and dual regression were applied. Coherence and mutual information extracted frequency/nonlinear time-domain features. Chi-square feature selection with forward search identified optimal features, evaluated across 4 machine learning (ML) models (Decision Trees, Naïve Bayes, support vector machine (SVM), and K-Nearest Neighbors (K-NN(). Bayesian optimization tuned hyperparameters, and hill-climbing determined feature inclusion. Time-domain features were classified using correlation. Furthermore, the Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to re-evaluate feature selection, assessing the impact of chi-square selected features on classification accuracy. Results: Chi-Squared feature selection with linear full correlation yielded 96.6% accuracy for males, while frequency domain selection using K-NN at specific frequencies achieved 86.7% accuracy for females. Comparison with existing methods: This study achieves comparable accuracy to previous work using fewer features. Prior research using t-test p-values saw male accuracy peak at 96.6% with 11 features and female accuracy at 93.3% with at least six, while this method reaches 86.7% accuracy with a single feature, outperforming single time-domain feature accuracy (83.3%). Conclusion: These results highlight the approach's effectiveness. This study showed that similar features at different frequencies can have varying discriminative power. Autism Spectrum Disorder Functional Connectivity Resting State fMRI (rs-fMRI) Coherence Classification Frequency Domain 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-7132626","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496198151,"identity":"275c1357-0bb6-410b-a95f-f2f640ae2144","order_by":0,"name":"Hossein Haghighat","email":"data:image/png;base64,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","orcid":"","institution":"Kashmar Higher Education Institute","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Haghighat","suffix":""}],"badges":[],"createdAt":"2025-07-15 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7132626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7132626/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90944925,"identity":"294a948a-6161-4db6-a31d-06833ba14eab","added_by":"auto","created_at":"2025-09-09 20:01:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":683283,"visible":true,"origin":"","legend":"","description":"","filename":"Main41.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7132626/v1_covered_4c0ea5ea-d91e-48da-9abb-af7e2094a698.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Techniques and Chi-square Feature Selection for Diagnostic Classification Model of Autism Spectrum Disorder Using fMRI Data","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 Spectrum Disorder, Functional Connectivity, Resting State fMRI (rs-fMRI), Coherence, Classification, Frequency Domain","lastPublishedDoi":"10.21203/rs.3.rs-7132626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7132626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Autism Spectrum Disorder (ASD) diagnosis relies on subjective observation, hindered by ASD's diverse presentation, symptom overlap, and sex-specific neurobiology, causing misdiagnosis, especially in females. Thus, objective and reliable diagnostic methods are critical.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNew method:\u003c/strong\u003e This rs-fMRI study built a sex-dependent ASD diagnostic classification model (DCM) using functional connectivity. After preprocessing, GICA and dual regression were applied. Coherence and mutual information extracted frequency/nonlinear time-domain features. Chi-square feature selection with forward search identified optimal features, evaluated across 4 machine learning (ML) models (Decision Trees, Naïve Bayes, support vector machine (SVM), and K-Nearest Neighbors (K-NN(). Bayesian optimization tuned hyperparameters, and hill-climbing determined feature inclusion. Time-domain features were classified using correlation. Furthermore, the Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to re-evaluate feature selection, assessing the impact of chi-square selected features on classification accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Chi-Squared feature selection with linear full correlation yielded 96.6% accuracy for males, while frequency domain selection using K-NN at specific frequencies achieved 86.7% accuracy for females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with existing methods:\u003c/strong\u003e This study achieves comparable accuracy to previous work using fewer features. 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