On the trade-off between fairness and performance in glioma grade prediction using pre-processing and post-processing techniques to mitigate bias

On the trade-off between fairness and performance in glioma grade prediction using pre-processing and post-processing techniques to mitigate bias | 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 Article On the trade-off between fairness and performance in glioma grade prediction using pre-processing and post-processing techniques to mitigate bias Raquel Sánchez-Marqués, Vicente García, J. Salvador Sánchez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661526/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract This research paper investigates the impact of demographic biases, specifically race and gender, on machine learning-based glioma grading using the TCGA data set compiled from The Cancer Genome Atlas. It applies three common classifiers (logistic regression, random forests, and extreme gradient boosting) and explores pre-processing (reweighting) and post-processing (equalized odds) strategies for bias mitigation. The study evaluates prediction performance (Matthews correlation coefficient, recall, specificity) and fairness (disparate impact, equal opportunity difference, error rate difference) metrics, highlighting trade-offs between fairness and accuracy across demographic groups. In the case of the most severe bias (race), the pre-trained logistic regression model using the reweighting algorithm shows some deterioration in prediction outcomes for the under-represented group and even an increase in unfairness, while the post-processing approach improves results for the under-represented group and provides significant fairness gains. These results are interesting because they could be taken into account in real-world clinical decision-making or in outcomes for under-represented patient groups. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 28 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Editor invited by journal 30 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 22 Sep, 2025 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-7661526","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529632153,"identity":"9030393b-d4c6-4e2a-9e11-2739389e3492","order_by":0,"name":"Raquel Sánchez-Marqués","email":"","orcid":"","institution":"MRC Unit The Gambia at London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Sánchez-Marqués","suffix":""},{"id":529632154,"identity":"147b0442-2ca5-4374-9925-f72960f12112","order_by":1,"name":"Vicente García","email":"","orcid":"","institution":"Universidad Autónoma de Ciudad Juárez","correspondingAuthor":false,"prefix":"","firstName":"Vicente","middleName":"","lastName":"García","suffix":""},{"id":529632155,"identity":"2c38d34a-8ef4-4f8f-9c49-c83507a58b54","order_by":2,"name":"J. 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bias","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific 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It applies three common classifiers (logistic regression, random forests, and extreme gradient boosting) and explores pre-processing (reweighting) and post-processing (equalized odds) strategies for bias mitigation. The study evaluates prediction performance (Matthews correlation coefficient, recall, specificity) and fairness (disparate impact, equal opportunity difference, error rate difference) metrics, highlighting trade-offs between fairness and accuracy across demographic groups. In the case of the most severe bias (race), the pre-trained logistic regression model using the reweighting algorithm shows some deterioration in prediction outcomes for the under-represented group and even an increase in unfairness, while the post-processing approach improves results for the under-represented group and provides significant fairness gains. These results are interesting because they could be taken into account in real-world clinical decision-making or in outcomes for under-represented patient groups.","manuscriptTitle":"On the trade-off between fairness and performance in glioma grade prediction using pre-processing and post-processing techniques to mitigate bias","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 10:30:50","doi":"10.21203/rs.3.rs-7661526/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T16:27:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T05:57:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T08:54:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311296281425479420881056086602396013736","date":"2025-11-17T17:59:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136639573573912775117077813686516407276","date":"2025-11-17T03:09:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T14:20:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145335269948069912216001059277475823514","date":"2025-10-27T16:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289859516670230591575082665882323935191","date":"2025-10-25T09:35:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T14:15:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T13:46:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-30T12:31:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T12:47:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-22T16:28:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8c88f74-2e1c-45c1-8394-df542ab14e62","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56297055,"name":"Biological sciences/Cancer"},{"id":56297056,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56297057,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-02-23T16:01:52+00:00","versionOfRecord":{"articleIdentity":"rs-7661526","link":"https://doi.org/10.1038/s41598-026-40555-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-18 15:58:25","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-10-20 10:30:50","video":"","vorDoi":"10.1038/s41598-026-40555-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40555-x","workflowStages":[]},"version":"v1","identity":"rs-7661526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7661526","identity":"rs-7661526","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}