PCOD Disease Detection Using Machine Learning

PCOD Disease Detection Using Machine Learning | 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 PCOD Disease Detection Using Machine Learning SULEKH KUMAR, Momita Kundu, Kamla Kumari, Prabhat Purushottam, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7117500/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: Polycystic Ovarian Disease (PCOD) is one of the most common endocrine disorders affecting women of reproductive age, with a prevalence of 6-12% globally. Early detection and accurate diagnosis remain challenging due to the heterogeneous nature of symptoms and the complexity of diagnostic criteria. Objective: This study aims to develop and evaluate machine learning models for automated PCOD detection using clinical, biochemical, and anthropometric parameters to improve diagnostic accuracy and enable early intervention. Methods: We developed a comprehensive machine learning framework incorporating four different algorithms: Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. The model was trained on a dataset of 1,000 cases with 13 clinical features including hormonal profiles, metabolic parameters, and symptom assessments. Model performance was evaluated using cross-validation, ROC-AUC analysis, and comprehensive statistical metrics. Results: The Gradient Boosting model achieved the highest performance with an AUC of 0.92, sensitivity of 88.5%, and specificity of 89.2%. Feature importance analysis revealed that LH/FSH ratio, testosterone levels, and menstrual irregularity were the most significant predictors. The model demonstrated robust performance across different patient demographics. Conclusion: Machine learning models show promising potential for PCOD detection, offering a reliable, cost-effective screening tool that could enhance clinical decision-making and improve patient outcomes through early intervention. PCOD PCOS Machine Learning Random Forest Gradient Boosting Hormonal Disorders Women's Health Diagnostic Models Full Text 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. 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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-7117500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493610193,"identity":"1e36a88f-258e-423d-acaf-ddce299fe6b8","order_by":0,"name":"SULEKH KUMAR","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFAC5gbGxoYEOFcORBx4gFcLI6oWY7CWBByKsWpJbACR+LTIz0hsfDhzR1ri2vazjz/z1NxLnx92+CHQFjs53QbsWgxuJDYbbjyTk7jtTLqZ5Ixjxbkbb6cZALUkG5sdwKFFIrFN8mFbReK2A2lsDB8bEnI3zk4AaTkAFMHpsPafYC3nnzF/SGxISDecnf4BrxaGG4ltjBvbgA67kcYgAbQlQV46B78tBmceNkvObEsz3nbjGRvQLwmGG6RzCg4kGOD2i3x78sGPvW3JstvOpzEDQyxBXn52+uYPHyrs5HBpwWIvWKUBscrB9jaQonoUjIJRMApGAgAA+XVsy8nyWHUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0006-6788-1115","institution":"RVS College of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"SULEKH","middleName":"","lastName":"KUMAR","suffix":""},{"id":493610194,"identity":"fc89d903-3517-4773-8164-e9b6b7e28e55","order_by":1,"name":"Momita Kundu","email":"","orcid":"","institution":"RVS College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Momita","middleName":"","lastName":"Kundu","suffix":""},{"id":493610195,"identity":"3eb6d24a-6775-47fa-8026-05379e0c0df4","order_by":2,"name":"Kamla Kumari","email":"","orcid":"","institution":"RVS College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kamla","middleName":"","lastName":"Kumari","suffix":""},{"id":493610196,"identity":"5db8a7ce-b720-4b38-9156-aaeff7d2d7de","order_by":3,"name":"Prabhat Purushottam","email":"","orcid":"","institution":"RVS College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Prabhat","middleName":"","lastName":"Purushottam","suffix":""},{"id":493610197,"identity":"8164e9aa-30f7-4963-8896-396146a11fd2","order_by":4,"name":"MD. Shamsher Alam","email":"","orcid":"","institution":"RVS College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"MD.","middleName":"Shamsher","lastName":"Alam","suffix":""}],"badges":[],"createdAt":"2025-07-14 06:20:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7117500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7117500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90267184,"identity":"865970e5-310f-41f1-a1c1-2434f0dbcfa8","added_by":"auto","created_at":"2025-08-31 18:12:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":458464,"visible":true,"origin":"","legend":"","description":"","filename":"PCODDiseaseDetectionUsingMachineLearning11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7117500/v1_covered_d65fa46e-096e-411c-96ff-bb56a9036555.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePCOD Disease Detection Using Machine Learning\u003c/p\u003e","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":"PCOD, PCOS, Machine Learning, Random Forest, Gradient Boosting, Hormonal Disorders, Women's Health, Diagnostic Models","lastPublishedDoi":"10.21203/rs.3.rs-7117500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7117500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Polycystic Ovarian Disease (PCOD) is one of the most common endocrine disorders affecting women of reproductive age, with a prevalence of 6-12% globally. Early detection and accurate diagnosis remain challenging due to the heterogeneous nature of symptoms and the complexity of diagnostic criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aims to develop and evaluate machine learning models for automated PCOD detection using clinical, biochemical, and anthropometric parameters to improve diagnostic accuracy and enable early intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We developed a comprehensive machine learning framework incorporating four different algorithms: Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. The model was trained on a dataset of 1,000 cases with 13 clinical features including hormonal profiles, metabolic parameters, and symptom assessments. 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The model demonstrated robust performance across different patient demographics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Machine learning models show promising potential for PCOD detection, offering a reliable, cost-effective screening tool that could enhance clinical decision-making and improve patient outcomes through early intervention.\u003c/p\u003e","manuscriptTitle":"PCOD Disease Detection Using Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 18:28:33","doi":"10.21203/rs.3.rs-7117500/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":"b90a3b66-c0b2-4076-b08e-3cd3dc0eb48c","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-31T18:03:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 18:28:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7117500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7117500","identity":"rs-7117500","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}