[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127956-en":3,"doc-seo-127956-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127956,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning approaches for predicting postpartum hemorrhage - a comprehensive systematic literature review","Postpartum hemorrhage (PPH) drives substantial maternal mortality, with a critical gap in identifying high-risk pregnant women early, as many cases remain undetected until severe bleeding occurs. A systematic review synthesizes evidence from 43 selected studies to map the most commonly used machine learning techniques for PPH prediction. The review emphasizes frequently applied models—including logistic regression, extreme gradient boosting, random forest, and decision trees—and frames how accurate prediction can support timely clinical intervention to lower PPH-related deaths.","Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review  \nDewi Pusparani Sinambela1,2, Bahbibi Rahmatullah1, Noor Hidayah Che Lah1,  \nAhmad Wiraputra Selamat1  \n1Faculty of Computing and Meta-Technology, Sultan Idris Education University, Perak, Malaysia 2Department of Midwifery, Faculty of Health, Sari Mulia University, Banjarmasin, Indonesia  \nArticle history:  \nReceived Jan 22, 2024 Revised Mar 4, 2024 Accepted Mar 16, 2024  \nKeywords:  \nArtificial intelligence Childbirth  \nMachine learning Maternal bleeding Postpartum hemorrhage Prediction  \nCorresponding Author:  \nPostpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.  \nThis is an open access article under the CC BY-SA license.  \nBahbibi Rahmatullah  \nFaculty of Computing and Meta-Technology, Sultan Idris Education University Perak, Malaysia  \n[Email: bahbibi@meta.upsi.edu.my](Email: bahbibi@meta.upsi.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nPostpartum hemorrhage (PPH) is excessive bleeding after childbirth, defined as over 500 ml after vaginal delivery or 1000 ml after a cesarean section [1]–[4]. A global health concern, PPH is the leading cause of maternal mortality, responsible for about 6% of all maternal deaths, with developing nations, especially lowincome countries, bearing the highest burden [5]–[8] . In Indonesia, maternal complications, including PPH, contribute significantly to maternal fatalities, with 1,280 reported cases [9], [10] .  \nEffectively managing PPH requires prompt recognition of risk factors and responding to excessive bleeding. Diagnosing PPH is challenging due to underestimated blood loss and variable presentation of risk factors. Current guidelines emphasize the importance of vigilance and proactive measures to address PPH [11] . Preventing PPH-related mortality involves timely identification, access to resources, and skilled healthcare providers. Mitigating risks can be achieved through predictive modeling for anticipating complications and implementing precautionary measures [12], [13] .  \nMachine learning (ML) models, driven by intelligent algorithms, show excellent performance in various domains [14] . In maternal health, ML, a subset of artificial intelligence, holds promise for improving predictions related to PPH. ML doesn't require explicit programming but leverages data on factors contributing to PPH [15]–[17] . Recently, ML algorithms have gained prominence in computer science research, particularly in maternal health. Various ML techniques automatically classify clinical data for disease diagnosis, showing  \ndiverse predictive performances [18], [19] . However, deploying ML algorithms in clinical settings poses challenges, including the potential obstacle of overfitting, affecting various prediction models [20] . ML tools with strong nonlinear fitting capabilities can model and analyze PPH. Trained on historical data, these models predict PPH likelihood, assess severity, and forecast outcomes. This aids early detection a","cbCaiufC1A0IOtTo","https://ap.wps.com/l/cbCaiufC1A0IOtTo","pdf",614936,2,1,9,"English","en",105,"# 1. INTRODUCTION\n## Definition and clinical burden of PPH\n## Rationale for predictive modeling\n# 2. METHOD\n## Review protocol and PRISMA-based SLR approach\n## Database and search strategy\n## Article selection procedure","[{\"question\":\"Why is postpartum hemorrhage risk prediction important?\",\"answer\":\"PPH is a leading cause of maternal mortality, and many high-risk cases are not identified early. Predictive models support earlier detection and timely intervention.\"},{\"question\":\"What machine learning methods are most frequently used for PPH prediction in the review?\",\"answer\":\"The review highlights logistic regression, extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) as the most frequently used methods.\"},{\"question\":\"How were studies selected for this systematic literature review?\",\"answer\":\"The study follows a PRISMA-based SLR protocol, using database searching with defined keywords and applying inclusion/exclusion criteria across publication years and study characteristics.\"}]","Machine learning approaches for predicting postpartum hemorrhage - a comprehensive systematic literature review | PDF",1785943261,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-approaches-for-predicting-postpartum-hemorrhage-a-comprehensive-systematic-literature-review","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-predicting-postpartum-hemorrhage-a-comprehensive-systematic-literature-review/127956/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is postpartum hemorrhage risk prediction important?","Question",{"text":76,"@type":77},"PPH is a leading cause of maternal mortality, and many high-risk cases are not identified early. Predictive models support earlier detection and timely intervention.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning methods are most frequently used for PPH prediction in the review?",{"text":81,"@type":77},"The review highlights logistic regression, extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) as the most frequently used methods.",{"name":83,"@type":74,"acceptedAnswer":84},"How were studies selected for this systematic literature review?",{"text":85,"@type":77},"The study follows a PRISMA-based SLR protocol, using database searching with defined keywords and applying inclusion/exclusion criteria across publication years and study characteristics.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]