[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126457-en":3,"doc-seo-126457-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126457,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",7,"Healthcare","A unified machine learning framework for gestational diabetes mellitus diagnosis","Pregnancy brings significant physiological changes, including elevated blood glucose that may culminate in gestational diabetes mellitus (GDM), caused by insufficient insulin production or ineffective insulin utilization. GDM is linked to serious risks for both mother and fetus, making accurate prediction and timely intervention essential. The study proposes a predictive framework validated on a small real-world Brazilian public-health cohort, combining ensemble machine learning and deep learning with data augmentation and a meta-classifier. Results show high AUC values across original imbalanced, balanced, and augmented datasets, with consistent performance in accuracy, precision, recall, and F1.","Hassan et al. Discover Applied Sciences (2026) 8:313  \n[https://doi.org/10.1007/s42452-025-08178-5](https://doi.org/10.1007/s42452-025-08178-5)  \nDiscover Applied Sciences  \nRESEARCH Open Access  \nA unified machine learning framework for gestational diabetes mellitus diagnosis  \nAhmad Hassan 1, Saima Gulzar Ahmad1, Ehsan Ullah Munir 1, Hassan Rabah2, Slavisa Jovanovic2 and Naeem Ramzan3*  \n*Correspondence:  \nNaeem Ramzan [naeem.ramzan@uws.ac.uk](naeem.ramzan@uws.ac.uk)[ ](naeem.ramzan@uws.ac.uk)1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Grand Trunk Road,  \nWah Cantonment Wah 47010, Pakistan  \n2Institute of Jean Lamour, University of Lorraine, Campus Artem, 2 allée André Guinier, BP 50840,  \nNancy Cedex 54011, France 3School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, PA1 2BE Paisley, UK  \nAbstract  \nPregnancy is an extraordinary journey marked by many bodily changes. One notable change is the rise in blood sugar levels, leading to a condition called gestational diabetes mellitus (GDM) . It happens when the body struggles to produce or effectively use insulin during pregnancy. Several health risks of GDM highlight the critical need for accurate prediction and timely intervention. To address this, the study presents a predictive framework validated on a small real-world cohort dataset from a Brazilian public health setting. The core of this research is a composite predictive model that integrates a diverse ensemble of machine learning and deep learning algorithms. In order to enrich the training material, a function was created to generate new instances based on initial dataset records. The framework’s ability to combine the strengths of various models and leverage a meta-classifier for final predictions was rigorously tested across multiple datasets. The results demonstrate exceptional performance by achieving high AUC scores of 88. 91%, 95. 55%, and 98. 71% on original imbalanced, balanced, and augmented datasets, respectively. Additionally, the model shows strong performance across other metrics, including accuracy, precision, recall, and F1 score. These findings validate the generalizability and robustness of the predictive framework. Furthermore, the paper outlines a practical application of this model within a remote-sensing framework in the management information system (MIS) at basic health units (BHUs) . It can facilitate proactive GDM management and improve maternal-fetal health outcomes in  \nlow-resource settings. The work showcases the predictive framework’s potential to improve GDM management and maternal-fetal health outcomes.  \nKeywords Gestational diabetes mellitus, Machine learning, Deep learning, Ensemble learning, Predictive analytics  \n1 Introduction  \nGDM condition represents a common metabolic disorder of pregnancy [1]. The reported global prevalence of the GDM ranges from 3.8 to 21%[2]. It is specifically distinguished by the onset of elevated blood glucose levels in previously non-diabetic women [3]. Commonly, this state usually normalizes following parturition [4]. The underlying pathophysiology involves increased insulin resistance [5], a phenomenon largely induced  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly fro","cbCaiiqgbwyfgZa1","https://ap.wps.com/l/cbCaiiqgbwyfgZa1","pdf",3559149,11,1,26,"English","en",105,"# Abstract\n# 1 Introduction\n## Background and prevalence of GDM\n## Health risks for mothers and fetuses\n## Motivation for ML prediction models","[{\"question\":\"What is gestational diabetes mellitus (GDM) and why is it clinically important?\",\"answer\":\"GDM is characterized by elevated blood glucose during pregnancy when the body cannot produce enough insulin or use it effectively. It increases risks for serious complications in both the mother and fetus.\"},{\"question\":\"How does the proposed framework make GDM predictions?\",\"answer\":\"It integrates an ensemble of machine learning and deep learning models with a meta-classifier for final predictions, and it uses a function to generate augmented training instances.\"},{\"question\":\"How well does the framework perform across different dataset settings?\",\"answer\":\"The framework achieves high AUC scores on original imbalanced, balanced, and augmented datasets, and it also shows strong performance on metrics such as accuracy, precision, recall, and F1 score.\"}]","A unified machine learning framework for gestational diabetes mellitus diagnosis | PDF",1785905158,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-unified-machine-learning-framework-for-gestational-diabetes-mellitus-diagnosis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-unified-machine-learning-framework-for-gestational-diabetes-mellitus-diagnosis/126457/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is gestational diabetes mellitus (GDM) and why is it clinically important?","Question",{"text":77,"@type":78},"GDM is characterized by elevated blood glucose during pregnancy when the body cannot produce enough insulin or use it effectively. It increases risks for serious complications in both the mother and fetus.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed framework make GDM predictions?",{"text":82,"@type":78},"It integrates an ensemble of machine learning and deep learning models with a meta-classifier for final predictions, and it uses a function to generate augmented training instances.",{"name":84,"@type":75,"acceptedAnswer":85},"How well does the framework perform across different dataset settings?",{"text":86,"@type":78},"The framework achieves high AUC scores on original imbalanced, balanced, and augmented datasets, and it also shows strong performance on metrics such as accuracy, precision, recall, and F1 score.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,120,125,130,133,137],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":118,"slug":119},40,"healthcare",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},8,"Research & Report",30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":134,"slug":136},10,"Lifestyle","lifestyle",{"id":138,"doc_module":4,"doc_module_name":47,"category_name":139,"show_sort_weight":108,"slug":140},19,"General","general"]