[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120907-en":3,"doc-seo-120907-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120907,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients","Early detection of type 2 diabetes is essential for preventing long-term complications, yet universal screening is not cost-effective. This study compares five machine learning models for classifying undiagnosed diabetes using heterogeneous NHANES data from 2005–2018. Undiagnosed diabetes is identified via biochemical confirmation with HbA1c across 45,431 participants, using simple clinically obtainable predictors. Model performance is assessed by AUC, PPV, NPV, and sensitivity.","Aalborg Universitet  \nA Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients  \nCichosz, Simon Lebech; Bender, Clara; Hejlesen, Ole  \nPublished in: Diabetology  \nDOI (link to publication from Publisher):  \n10.3390/diabetology5010001  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nCichosz, S. L. , Bender, C. , & Hejlesen, O. (2024) . A Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients. Diabetology, 5(1), 1-11.  \n[https://doi.org/10.3390/diabetology5010001](https://doi.org/10.3390/diabetology5010001)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nArticle  \nA Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients  \nSimon Lebech Cichosz *, Clara Bender  and Ole Hejlesen  \nCitation: Cichosz, S.L.; Bender, C.; Hejlesen, O. A Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients. Diabetology 2024, 5, 1–11 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diabetology5010001  \nAcademic Editors: Andrej Belanˇci´c, Sanja Klobuˇcar and Dario Raheli´c  \nReceived: 20 November 2023  \nRevised: 21 December 2023  \nAccepted: 21 December 2023  \nPublished: 3 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Health Science and Technology, Aalborg University, 9000 Aalborg, Denmark  \n* Correspondence: [simcich@hst.aau.dk](simcich@hst.aau.dk); Tel.: +45-9940-2020; Fax: +45-9815-4008  \nAbstract: Introduction: Early detection of type 2 diabetes is essential for preventing long-term complications. However, screening the entire population for diabetes is not cost-effective, so identifying individuals at high risk for this disease is crucial. The aim of this study was to compare the performance of five diverse machine learning (ML) models in classifying undiagnosed diabetes using large heterogeneous datasets. Methods: We used machine learning data from several years of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 to identify people with undiagnosed diabetes. The dataset included 45,431 participants, and biochemical confirmation of glucose control (HbA1c) were used to identify undiagnosed diabetes. The predictors were based on simple and clinically obtainable variables, which could be feasible for prescreening for diabetes. We included five ML models for comparison: random forest, AdaBoost, RUSBoost, LogitBoost, and a neural network. Results: The prevalence of undiagnosed diabetes was 4% . For the classification of undiagnosed diabetes, the area under the ROC curve (AUC) values were between 0.776 and 0.806 . The posi","cbCaijN1Vu8U4L9L","https://ap.wps.com/l/cbCaijN1Vu8U4L9L","pdf",1442760,1,12,"English","en",105,"# Introduction\n# Methods\n## Data source and cohort\n## Outcome definition\n## Models compared\n# Results\n## Prevalence and diagnostic performance\n# Conclusion","[{\"question\":\"What was the goal of this study?\",\"answer\":\"To compare the performance of five machine learning models for classifying undiagnosed diabetes, supporting risk-based prescreening.\"},{\"question\":\"How did the study identify undiagnosed diabetes?\",\"answer\":\"Undiagnosed diabetes was identified using biochemical confirmation of glucose control with HbA1c.\"},{\"question\":\"Which models were compared and how did they perform overall?\",\"answer\":\"The study compared random forest, AdaBoost, RUSBoost, LogitBoost, and a neural network, achieving AUC values between 0.776 and 0.806 with high NPVs (0.984–0.99) and sensitivities of 0.742–0.871.\"}]","A Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients | PDF",1785732623,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comparative-analysis-of-machine-learning-models-for-the-detection-of-undiagnosed-diabetes-patients","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-comparative-analysis-of-machine-learning-models-for-the-detection-of-undiagnosed-diabetes-patients/120907/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the goal of this study?","Question",{"text":75,"@type":76},"To compare the performance of five machine learning models for classifying undiagnosed diabetes, supporting risk-based prescreening.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did the study identify undiagnosed diabetes?",{"text":80,"@type":76},"Undiagnosed diabetes was identified using biochemical confirmation of glucose control with HbA1c.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models were compared and how did they perform overall?",{"text":84,"@type":76},"The study compared random forest, AdaBoost, RUSBoost, LogitBoost, and a neural network, achieving AUC values between 0.776 and 0.806 with high NPVs (0.984–0.99) and sensitivities of 0.742–0.871.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]