[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124889-en":3,"doc-seo-124889-105":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},124889,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Ensemble Machine Learning Framework for Predicting Maternal Health Risk during Pregnancy","Maternal health risks during pregnancy can trigger serious complications, including hypertensive disorders, abnormal glucose levels, depression, anxiety, and related conditions. This research leverages real-world datasets to identify and predict Maternal Health Risk (MHR) factors, aiming to support earlier recognition and monitoring of risk determinants. A Quad-Ensemble Machine Learning framework (QEML-MHRC) integrates multiple ML models with four ensemble techniques. Exploratory analysis highlights high blood pressure, low blood pressure, and high blood sugar as key predictors.","Citation:  \nKhadidos, A and Saleem, F and Selvarajan, S and Ullah, Z and Khadidos, A (2024) Ensemble Machine Learning Framework for Predicting Maternal Health Risk during Pregnancy. Scientific Reports, 14. ISSN 2045-2322 DOI: [https://doi.org/10.1038/s41598-024-71934-x](https://doi.org/10.1038/s41598-024-71934-x)  \nLink to Leeds Beckett Repository record:  \n[https://eprints.leedsbeckett.ac.uk/id/eprint/11353/](https://eprints.leedsbeckett.ac.uk/id/eprint/11353/)  \nDocument Version:  \nArticle (Published Version)  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \n The Author(s) 2024  \nThe aim of the Leeds Beckett Repository is to provide open access to our research, as required by funder policies and permitted by publishers and copyright law.  \nThe Leeds Beckett repository holds a wide range of publications, each of which has been checked for copyright and the relevant embargo period has been applied by the Research Services team.  \nWe operate on a standard take-down policy. If you are the author or publisher of an output and you would like it removed from the repository, please contact us and we will investigate on a case-by-case basis.  \nEach thesis in the repository has been cleared where necessary by the author for third party copyright. If you would like a thesis to be removed from the repository or believe there is an issue with copyright, please contact us on [openaccess@leedsbeckett.ac.uk](openaccess@leedsbeckett.ac.uk) and we will investigate on a case-by-case basis.  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnsemble machine learning framework for predicting maternal health risk during pregnancy  \nAlaa O. Khadidos1,2, Farrukh Saleem3, Shitharth Selvarajan4,5*, Zahid Ullah6 & Adil O. Khadidos7  \nMaternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models  \nused in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect highrisk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.  \nKeywords Maternal health risk, Machine learning, Ensemble","cbCaiqlEBNOAF5JO","https://ap.wps.com/l/cbCaiqlEBNOAF5JO","pdf",3539699,1,21,"English","en",105,"# Overview\n## Maternal health risk and motivation\n## Proposed Quad-Ensemble framework (QEML-MHRC)\n## Data, exploratory analysis, and key risk factors\n## Evaluation approach and class-wise performance","[{\"question\":\"What problem does this research address in maternal health during pregnancy?\",\"answer\":\"It addresses the need to identify and predict Maternal Health Risk (MHR) factors that can lead to pregnancy complications such as high blood pressure, abnormal glucose levels, depression, and anxiety.\"},{\"question\":\"What is the Quad-Ensemble Machine Learning framework (QEML-MHRC)?\",\"answer\":\"It is an ensemble ML approach that combines multiple machine learning models with four ensemble techniques to improve MHR prediction and classification performance.\"},{\"question\":\"Which risk factors are highlighted as most significant in exploratory analysis?\",\"answer\":\"The study reports high blood pressure, low blood pressure, and high blood sugar levels as prominent risk factors for pregnant women.\"}]","Ensemble Machine Learning Framework for Predicting Maternal Health Risk during Pregnancy | PDF",1785895239,53,{"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},"ensemble-machine-learning-framework-for-predicting-maternal-health-risk-during-pregnancy","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ensemble-machine-learning-framework-for-predicting-maternal-health-risk-during-pregnancy/124889/",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","2026-08-05",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 problem does this research address in maternal health during pregnancy?","Question",{"text":75,"@type":76},"It addresses the need to identify and predict Maternal Health Risk (MHR) factors that can lead to pregnancy complications such as high blood pressure, abnormal glucose levels, depression, and anxiety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the Quad-Ensemble Machine Learning framework (QEML-MHRC)?",{"text":80,"@type":76},"It is an ensemble ML approach that combines multiple machine learning models with four ensemble techniques to improve MHR prediction and classification performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which risk factors are highlighted as most significant in exploratory analysis?",{"text":84,"@type":76},"The study reports high blood pressure, low blood pressure, and high blood sugar levels as prominent risk factors for pregnant women.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]