Diagnosis of PCOS in Adolescent Girls Using Traditional and Ensemble Machine Learning Methods

Diagnosis of PCOS in Adolescent Girls Using Traditional and Ensemble Machine Learning Methods | 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 Diagnosis of PCOS in Adolescent Girls Using Traditional and Ensemble Machine Learning Methods Priyanka Pariyawala, Pushpal Y Desai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7009734/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 Polycystic ovary syndrome (PCOS) affects women of childbearing age. PCOS is a condition where women have irregular menses, obesity, hyperandrogenism, type 2 diabetes mellitus, cardiovascular diseases, etc. Awareness of PCOS has been one of the most important concerns for female fertility as PCOS patient has different risks associated with health, among the above-listed risks, the major risk is infertility. In this study, we developed a Model for predicting PCOS among adolescent girls. The questionnaire was prepared by considering different symptoms related to PCOS and applying machine-learning techniques to detect and raise awareness of PCOS. A total of 21 features were available then feature selection techniques 14 best features among them were selected, we used, SMOTE(Synthetic Minority Oversampling Techniques) with under sampling to solve the problem of class imbalance, and then traditional and Ensemble ML models were created, we compared output from both conventional and ensemble models, and select the best model among them. We split the dataset 80:20, and results showed that the ensemble technique did better than a traditional method, where Extra Trees had 91.70% and BRF achieved an accuracy of 90.30%. Machine Learning Polycystic ovarian syndrome (PCOS feature selection disease prediction 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-7009734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494809890,"identity":"02dcbbb4-7610-4a21-8b0f-4c9e4db9e413","order_by":0,"name":"Priyanka Pariyawala","email":"data:image/png;base64,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","orcid":"","institution":"Veer Narmad South Gujarat University","correspondingAuthor":true,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Pariyawala","suffix":""},{"id":494809891,"identity":"4e55f72c-9f19-4f31-bddf-857e62f9a63c","order_by":1,"name":"Pushpal Y Desai","email":"","orcid":"","institution":"Veer Narmad South Gujarat University","correspondingAuthor":false,"prefix":"","firstName":"Pushpal","middleName":"Y","lastName":"Desai","suffix":""}],"badges":[],"createdAt":"2025-06-30 10:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7009734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7009734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97777910,"identity":"57aef8db-db20-455e-91ad-996421142d35","added_by":"auto","created_at":"2025-12-09 09:09:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1657148,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedJournalSentPriyankaPCOS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7009734/v1_covered_1b6d5a5e-f842-4be9-8784-291bbb2f1873.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnosis of PCOS in Adolescent Girls Using Traditional and Ensemble Machine Learning Methods","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":"Machine Learning, Polycystic ovarian syndrome (PCOS, feature selection, disease prediction","lastPublishedDoi":"10.21203/rs.3.rs-7009734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7009734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolycystic ovary syndrome (PCOS) affects women of childbearing age. PCOS is a condition where women have irregular menses, obesity, hyperandrogenism, type 2 diabetes mellitus, cardiovascular diseases, etc. Awareness of PCOS has been one of the most important concerns for female fertility as PCOS patient has different risks associated with health, among the above-listed risks, the major risk is infertility. In this study, we developed a Model for predicting PCOS among adolescent girls. The questionnaire was prepared by considering different symptoms related to PCOS and applying machine-learning techniques to detect and raise awareness of PCOS. A total of 21 features were available then feature selection techniques 14 best features among them were selected, we used, SMOTE(Synthetic Minority Oversampling Techniques) with under sampling to solve the problem of class imbalance, and then traditional and Ensemble ML models were created, we compared output from both conventional and ensemble models, and select the best model among them. We split the dataset 80:20, and results showed that the ensemble technique did better than a traditional method, where Extra Trees had 91.70% and BRF achieved an accuracy of 90.30%.\u003c/p\u003e","manuscriptTitle":"Diagnosis of PCOS in Adolescent Girls Using Traditional and Ensemble Machine Learning Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 21:46:37","doi":"10.21203/rs.3.rs-7009734/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a3711bf1-3566-4d79-9233-59e486eb0f33","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T09:09:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 21:46:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7009734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7009734","identity":"rs-7009734","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}