[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126020-en":3,"doc-seo-126020-105":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},126020,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Proposed model to predict preeclampsia using machine learning approach - Article highlights","Preeclampsia is a major cause of maternal death and remains more prevalent in developing countries, with no meaningful reduction in incidence over recent decades. To support earlier prevention and improve prenatal risk screening, the research proposes an early-detection model using machine learning and retrospective data. Results show a decision tree achieves 92.2% accuracy (AUC 0.91, specificity 92.3, sensitivity 83.6). The top contributing features are prior hypertension, prior diabetes mellitus, and prior preeclampsia, enabling technology-assisted monitoring for health workers.","Proposed model to predict preeclampsia using machine learning  \napproach  \nRaden Topan Aditya Rahman1,2, Muhammad Modi Lakulu3, Ismail Yusuf Panessai3, Esti Yuandari4, Ika Mardiatul Ulfa5, Fitriani Ningsih5, Lensi Natalia Tambunan5  \n1Doctoral Program Student, Faculty of Computing and Meta Technology, Sultan Idris Education University, Tanjung Malim, Malaysia 2Polytechnic of Health Borneo Citra Medika, Tanah Laut Regency, Indonesia 3Faculty of Computing and Meta Technology, Sultan Idris Education University, Tanjung Malim, Malaysia 4Faculty of Health, Sari Mulia University, Banjarmasin, Indonesia  \n5Betang ASI Raya Midwifery Academy, Pahandut, Indonesia  \nArticle history:  \nReceived Mar 8, 2024 Revised Jun 10, 2024 Accepted Jun 25, 2024  \nKeywords:  \nArtificial intelligence Machine learning Proposed model  \nPrediction  \nPreeclampsia  \nCorresponding Author:  \nPregnancy complications, which are the biggest cause of death in productive women, are more common in developing countries with low incomes. One of the contributors to death in pregnant women is preeclampsia which contributes 2-8% every day. Based on research results, more than 70% of the use of technology can be a solution for early prevention in detecting cases of pregnancy. The aim of this research is to build a model for early detection of preeclampsia using a machine learning approach. Sample using retrospective data with sample size 1.473. Based on the result, decision tree (DT) is the best model with accuracy 92.2%(area under curve (AUC): 0.91; Spec: 92.3; and Sens: 83.6), according to weigh correlation we can show 3 (three) highest features causes preeclampsia is history of hypertension, history of diabetes mellitus, and history of preeclampsia. The health of pregnant women is essential in the development of the fetus, so it needs optimal monitoring. Monitoring during pregnancy can now be done through technology-based examinations for assist health workers in making decisions during pregnancy.  \nThis is an open access article under the CC BY-SA license.  \nRaden Topan Aditya Rahman  \nDoctoral Program Student, Faculty of Computing and Meta Technology, Sultan Idris Education University 35900 Tanjung Malim, Perak, Malaysia  \nEmail: [topanaditya85@gmail.com](topanaditya85@gmail.com), [modi@meta.upsi.edu.my](modi@meta.upsi.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nPreeclampsia is one of cause of maternal death after bleeding. World Health Organization (WHO) estimates that the cases of preeclampsia are seven times higher in developing countries than in developed countries. In the last two decades, there has been no significant decrease in the incidence of preeclampsia. The magnitude of this problem is not only because preeclampsia has an impact on the mother during pregnancy and childbirth, but also causes post-partum problems due to endothelial dysfunction in various organs, such as the risk of cardiometabolic diseases and other complications. Thus, preeclampsia becomes a serious medical problem and complex [1] . There is no active screening evaluation for preeclampsia so that efforts to prevent preeclampsia are not optimal which can lead to increased morbidity and mortality. Therefore, there is an urgency for recommendations based on scientific evidence to assist practitioners in diagnosing, evaluating, and managing preeclampsia.  \nTo date, there have been various biomarker findings that can be used to predict the incidence of preeclampsia, but no single test has high sensitivity and specificity. This occurs because the features used are  \nmostly related to characteristics. A series of checkups using more features is urgently needed through technological assistance to screen the risk of preeclampsia in pregnant women from the start of their pregnancy, so health practitioners can identify risk factors for preeclampsia and control them as a form of primary prevention [2] . Pregnancy screening at dr. H. Moch Ansari Saleh General Hospital is currently still being ","cbCaimDG6CmOawr2","https://ap.wps.com/l/cbCaimDG6CmOawr2","pdf",521573,5,1,9,"English","en",105,"# ABSTRACT\n# 1. INTRODUCTION\n## Maternal risk and global prevalence\n## Limitations of screening and need for evidence-based guidance\n## Current hospital screening practice and incidence trends\n## Role of machine learning in clinical prediction","[{\"question\":\"What is the main goal of the proposed research model?\",\"answer\":\"To build a model for early detection of preeclampsia using a machine learning approach.\"},{\"question\":\"Which machine learning model performed best in the study?\",\"answer\":\"The decision tree (DT) model, with accuracy 92.2% (AUC 0.91, specificity 92.3, sensitivity 83.6).\"},{\"question\":\"What are the three highest features linked to preeclampsia risk?\",\"answer\":\"History of hypertension, history of diabetes mellitus, and history of preeclampsia.\"}]","Proposed model to predict preeclampsia using machine learning approach - Article highlights | PDF",1785902593,23,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"proposed-model-to-predict-preeclampsia-using-machine-learning-approach-article-highlights","",{"@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/proposed-model-to-predict-preeclampsia-using-machine-learning-approach-article-highlights/126020/",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","2026-08-23","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 the main goal of the proposed research model?","Question",{"text":77,"@type":78},"To build a model for early detection of preeclampsia using a machine learning approach.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning model performed best in the study?",{"text":82,"@type":78},"The decision tree (DT) model, with accuracy 92.2% (AUC 0.91, specificity 92.3, sensitivity 83.6).",{"name":84,"@type":75,"acceptedAnswer":85},"What are the three highest features linked to preeclampsia risk?",{"text":86,"@type":78},"History of hypertension, history of diabetes mellitus, and history of preeclampsia.","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},[95,99,103,107,111,116,119,124,128,131,135],{"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":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"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":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",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":20,"slug":138},19,"General","general"]