[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120441-en":3,"doc-seo-120441-105":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},120441,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Preeclampsia prediction via machine learning - a systematic literature review","Preeclampsia is a life-threatening condition in late pregnancy with unclear causes and risk factors. Machine learning enables early prediction, motivating a systematic review of state-of-the-art studies. Articles published from January 1, 2013 to December 31, 2023 were screened from Google Scholar and PubMed, yielding 35 selected studies from 183. Common predictive features include maternal age, parity, BMI, diabetes, hypertension, and blood pressure, while medications, genetic data, and clinical imaging appear less often. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Naïve Bayes dominate, with research concentrated mainly in China and the USA and often based on small single-hospital datasets.","Health Systems  \nISSN: 2047-6965 (Print) 2047-6973 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/thss20)[www.tandfonline.com/journals/thss20](homepage: www.tandfonline.com/journals/thss20)  \nPreeclampsia prediction via machine learning: a systematic literature review  \nMert Özcan & Serhat Peker  \nTo cite this article: Mert Özcan & Serhat Peker (09 Dec 2024): Preeclampsia  \nprediction via machine learning: a systematic literature review, Health Systems, DOI: 10. 1080/20476965 .2024.2435845  \nTo link to this article: [https://doi.org/10.1080/20476965.2024.2435845](https://doi.org/10.1080/20476965.2024.2435845)  \n Published online: 09 Dec 2024.  \n\n|  Submit your article to this journal  |  |\n| --- | --- |\n|  | Article views: 464 |\n|  | View related articles  |\n|  View Crossmark data |  |\n|  Citing articles: 1 View citing articles  |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=thss20](https://www.tandfonline.com/action/journalInformation?journalCode=thss20)  \nHEALTH SYSTEMS  \n[https://doi.org/10.1080/20476965.2024.2435845](https://doi.org/10.1080/20476965.2024.2435845)  \nREVIEW ARTICLE  \nPreeclampsia prediction via machine learning: a systematic literature review  \nMert Özcan  and Serhat Peker   \nDepartment of Management Information Systems, İzmir Bakırçay University, İzmir, Türkiye  \nABSTRACT  \nPreeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. Wereviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Naïve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.  \nARTICLE HISTORY  \nReceived 7 February 2024 Accepted 20 November 2024  \nKEYWORDS  \nPreeclampsia; artificial intelligence; machine learning; deep learning; pregnancy  \n1. Introduction  \nWorld Health Organization (WHO) declared that, around 800 women face preventable mortality daily due to complications related to pregnancy and childbirth (Bertini et al., 2022) . This equates to one maternal death approximately every two minutes. These staggering issues predominantly impact low and middle-income countries, where an estimated 95% of these deaths occur (WHO, 2023a) . Maternal mortality is primarily due to immediate causes, which include direct obstetric complications such as postpartum haemorrhage, hypertensive disorders (preeclampsia oreclampsia), pregnancy-associated infections and complications from unsafe abortion. Indirect causes, such as the onset of pre-existing medical conditions during pregnancy, are also significant (WHO, 2023b) .  \nPreeclampsia is a major contributor to maternal and perinatal mortality, long-term disability and associated health complications. Preeclampsia affects 2% − 8% of pregnancies (Steegers et al., 2010). Early prediction and effective management of preeclampsia can significantly improve outcomes for both mothers and infants. However, despite advanced healthcar","cbCaihBIclW9m0yR","https://ap.wps.com/l/cbCaihBIclW9m0yR","pdf",2500957,1,16,"English","en",105,"# Introduction\n# Machine learning approaches for prediction\n# Predictive features and clinical variables\n# Algorithms used across studies\n# Geographic distribution and research trends\n# Data limitations and future directions","[{\"question\":\"What was the scope of the systematic literature review?\",\"answer\":\"The review analyzed studies published between January 1, 2013 and December 31, 2023, using searches from Google Scholar and PubMed. From 183 identified studies, 35 met inclusion and exclusion criteria.\"},{\"question\":\"Which predictive features are most commonly used by machine learning models?\",\"answer\":\"Frequently used features include maternal age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure.\"},{\"question\":\"Why does the review highlight a need for further research?\",\"answer\":\"Many studies rely on small datasets from single hospitals, and the review emphasizes expanding toward more diverse and comprehensive research to improve early detection and management.\"}]","Preeclampsia prediction via machine learning - a systematic literature review | PDF",1785730134,40,{"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},"preeclampsia-prediction-via-machine-learning-a-systematic-literature-review","",{"@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/preeclampsia-prediction-via-machine-learning-a-systematic-literature-review/120441/",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 scope of the systematic literature review?","Question",{"text":75,"@type":76},"The review analyzed studies published between January 1, 2013 and December 31, 2023, using searches from Google Scholar and PubMed. From 183 identified studies, 35 met inclusion and exclusion criteria.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which predictive features are most commonly used by machine learning models?",{"text":80,"@type":76},"Frequently used features include maternal age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the review highlight a need for further research?",{"text":84,"@type":76},"Many studies rely on small datasets from single hospitals, and the review emphasizes expanding toward more diverse and comprehensive research to improve early detection and management.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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"]