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While risk prediction can use traditional statistics or machine learning, accuracy remains limited. This study compares multiple supervised machine learning algorithms to develop GDM risk prediction models and determine the optimal approach. Performance is evaluated using calibration, discrimination metrics, and decision curve analysis on routine antenatal care data from January 2016 to June 2021.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparison-of-machine-learning-and-conventional-logistic-regression-based-prediction-models-for-gestational-diabetes-in-an-ethnically-diverse-population-the-monash-gdm-machine-learning-model-article-abstract/290452/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparison-of-machine-learning-and-conventional-logistic-regression-based-prediction-models-for-gestational-diabetes-in-an-ethnically-diverse-population-the-monash-gdm-machine-learning-model-article-abstract/290452.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early identification of gestational diabetes risk important?","Question",{"text":113,"@type":114},"Early identification supports timely lifestyle interventions that can prevent GDM and lower associated maternal and neonatal complications.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is compared in this study?",{"text":118,"@type":114},"The study compares multiple machine learning algorithms against a conventional logistic regression approach for predicting GDM risk.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model achieved the best predictive performance and how was it evaluated?",{"text":122,"@type":114},"The CatBoost Classifier performed best in internal validation, evaluated using calibration and discrimination metrics and decision curve analysis, with detailed performance measures reported for a default cut-off point.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290452,1789641720,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","International Journal of Medical Informatics 179 (2023) 105228  \nContents lists available at ScienceDirect  \nInternational Journal of Medical Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ijmedinf)[ www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf)  \n| Comparison of machine learning and conventional logistic regression-based   prediction models for gestational diabetes in an ethnically diverse\u003Cbr>population; the Monash GDM Machine learning model\u003Cbr>Yitayeh Belstia, e, Lisa Morana, Lan Dud, Aya Mousaa, Kushan De Silva c, Joanne Enticotta, *, Helena Teedea, b, *\u003Cbr>a Monash Centre for Health Research and Implementation (MCHRI), Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia b Monash Health, Melbourne, Australia\u003Cbr>c Department of Radiation Sciences, Faculty of Medicine, Umeå University, Sweden d Monash University, Faculty of Information Technology\u003Cbr>e University of Gondar, College of Medicine and Health Science, Ethiopia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Machine learning Predictive model Prognosis\u003Cbr>Gestational diabetes mellitus |  | Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal.\u003Cbr>Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM.\u003Cbr>Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care ata large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed.\u003Cbr>Results: Upon internal validation, the machine learning and logistic regression model’s area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39).\u003Cbr>Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables. |\n\n1. Background  \nGestational diabetes mellitus (GDM) is a condition in which the body is unable to utilize insulin effectively, leading to insulin resistance and glucose intolerance [1]. It is defined as any level of impaired glucose  \ntolerance that appears or is first detected during pregnancy [2] and is increasing globally, with up to 1 in 6 pregnancies now affected [3–7]. This is predominantly due to increasing risk factors including rising obesity, gestational weight gain and advancing maternal age [8–11]. GDM increases the risk of adverse outcomes, including stillbirth,  \n* Corresponding authors at: Monash Centre for Health Research and Implementation (MCH","cbCailLUxLYSour7","https://ap.wps.com/l/cbCailLUxLYSour7","pdf",6043758,12,"English","# Background\n# Objective\n# Methods\n# Results\n# Conclusions","[{\"question\":\"Why is early identification of gestational diabetes risk important?\",\"answer\":\"Early identification supports timely lifestyle interventions that can prevent GDM and lower associated maternal and neonatal complications.\"},{\"question\":\"What is compared in this study?\",\"answer\":\"The study compares multiple machine learning algorithms against a conventional logistic regression approach for predicting GDM risk.\"},{\"question\":\"Which model achieved the best predictive performance and how was it evaluated?\",\"answer\":\"The CatBoost Classifier performed best in internal validation, evaluated using calibration and discrimination metrics and decision curve analysis, with detailed performance measures reported for a default cut-off point.\"}]","Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model - Article Abstract | PDF"]