A Model Combining CTGAN-Based Outlier Detection Mechanism with Ensemble Learning to Mitigate Type II Errors in Diabetes Detection

A Model Combining CTGAN-Based Outlier Detection Mechanism with Ensemble Learning to Mitigate Type II Errors in Diabetes Detection | 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 A Model Combining CTGAN-Based Outlier Detection Mechanism with Ensemble Learning to Mitigate Type II Errors in Diabetes Detection Dongxiang Liu, Zhanfei Ma, Xuebao Li, Bisheng Wang, Jing Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5706254/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 In the field of machine learning for diabetes detection, outliers in datasets remain a significant challenge. Traditional outlier handling methods often fall short in terms of accuracy and are prone to Type II errors. Moreover, these conventional approaches typically discard outliers, leading to inefficient data utilization. To address these limitations, this study aims to develop a more effective unsupervised outlier detection mechanism by integrating Conditional Generative Adversarial Networks (CTGAN) with Autoencoders. We further introduce a secondary outlier detection layer based on the Outlier Factor to enhance detection accuracy and reduce Type II errors. Additionally, we incorporate this mechanism into an ensemble learning framework and propose a novel training method for base learners that retains rather than discards outliers. The resulting model architecture is capable of simultaneously performing outlier detection and diabetes classification tasks. Our method demonstrates exceptional performance on eight outlier detection datasets and three diabetes classification datasets. Ablation studies confirm that the proposed dual outlier detection mechanism effectively mitigates Type II errors. Experimental results show that, compared to traditional methods, the proposed approach achieves significant improvements in outlier detection accuracy, reduction of Type II errors, and enhanced data utilization efficiency for diabetes detection models. Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes Health sciences/Signs and symptoms/Skin manifestations Ensemble learning Outlier Detection Clinical Prediction Models Diabetes 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. 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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-5706254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434769154,"identity":"c5d2b369-cf91-48b6-8927-d3d01343604b","order_by":0,"name":"Dongxiang Liu","email":"","orcid":"","institution":"Inner Mongolia University of Science and Technology Baotou Teachers' College","correspondingAuthor":false,"prefix":"","firstName":"Dongxiang","middleName":"","lastName":"Liu","suffix":""},{"id":434769155,"identity":"17d3f8a8-2b17-4cb7-8e47-2836f7ed6b71","order_by":1,"name":"Zhanfei 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Traditional outlier handling methods often fall short in terms of accuracy and are prone to Type II errors. Moreover, these conventional approaches typically discard outliers, leading to inefficient data utilization. To address these limitations, this study aims to develop a more effective unsupervised outlier detection mechanism by integrating Conditional Generative Adversarial Networks (CTGAN) with Autoencoders. We further introduce a secondary outlier detection layer based on the Outlier Factor to enhance detection accuracy and reduce Type II errors. Additionally, we incorporate this mechanism into an ensemble learning framework and propose a novel training method for base learners that retains rather than discards outliers. The resulting model architecture is capable of simultaneously performing outlier detection and diabetes classification tasks. Our method demonstrates exceptional performance on eight outlier detection datasets and three diabetes classification datasets. 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