AdvancedHybridNet: An AI-Powered Hybrid Ensemble for High-Accuracy Thyroid Disease Diagnosis Using Dynamic Feature Selection

AdvancedHybridNet: An AI-Powered Hybrid Ensemble for High-Accuracy Thyroid Disease Diagnosis Using Dynamic Feature Selection | 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 AdvancedHybridNet: An AI-Powered Hybrid Ensemble for High-Accuracy Thyroid Disease Diagnosis Using Dynamic Feature Selection Ateeq Ur, Muhammad Asif, Kaleem Ullah Qasim, Mohamad Khairi Ishak, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7401588/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 Thyroid diseases are increasingly prevalent, making early and accurate diagnosis critical to reducing mortality and complications. This study proposes Advanced-HybridNet, a novel machine learning framework that combines dynamic feature selection and an AI-driven ensemble learning approach to enhance thyroid disorder classification. By integrating DynamicRankSelector and a balanced class 1 sampling mechanism, the model effectively handles class imbalance and high-dimensional data, improving both robustness and interpretability. The ensemble comprises multiple optimized classifiers and leverages soft voting to enhance pre-dictive accuracy. The model achieves a sensitivity of 100% and an accuracy of 99.95%, outperforming conventional diagnostic methods. Additionally, random oversampling addresses class imbalance for minority conditions like hyperthy-roidism and hypothyroidism, leading to more stable predictions. Mathematical analysis further confirms the model’s high precision, reduced false positives, and consistent performance. These results suggest that AdvancedHybridNet offers a reliable, scalable, and more advanced diagnostic alternative to existing clinical techniques for thyroid disease prediction. Health issues Thyroid Disease Diagnosis Hybrid Feature Selection Artificial Intelligence Ensemble Learning Machine Learning AdvancedHybridNet Algorithm & Deep learning 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-7401588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503987523,"identity":"5b7928a0-af3c-4e79-847b-5b74d9a381de","order_by":0,"name":"Ateeq Ur","email":"","orcid":"","institution":"National Textile University","correspondingAuthor":false,"prefix":"","firstName":"Ateeq","middleName":"","lastName":"Ur","suffix":""},{"id":503987524,"identity":"3a0a1122-9a76-4825-9311-98af629211b3","order_by":1,"name":"Muhammad Asif","email":"","orcid":"","institution":"National Textile 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