[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128147-en":3,"doc-seo-128147-105":30,"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":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},128147,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa","Maternal and child health remains a critical global challenge, especially in low- and middle-income settings where skilled birth attendants are limited. Reducing home delivery is essential to lower maternal mortality. Evidence on forecasting home delivery after antenatal care (ANC) using machine learning in East Africa is limited. This study applies supervised machine-learning methods to Demographic and Health Survey data from 12 East African countries to predict home delivery and identify key drivers through SHAP.","TYPE Original Research PUBLISHED 05 June 2025  \nDOI 10.3389/fgwh.2025.1461475  \nEDITED BY  \nSarosh Iqbal,  \nForman Christian College, Pakistan  \nREVIEWED BY  \nKanchan Thapa,  \nNoble Shivapuri Research Institute, Nepal Adauto Barbosa,  \nFluminense Federal University, Brazil  \n*CORRESPONDENCE  \nAgmasie Damtew Walle  \n [agmasie89@gmail.com](agmasie89@gmail.com)  \nRECEIVED 16 July 2024  \nACCEPTED 02 May 2025  \nPUBLISHED 05 June 2025  \nCITATION  \nWalle AD, Kebede SD, Adem JB and Mamo DN (2025) Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa.  \nFront. Glob. Women’s Health 6:1461475 . doi: 10.3389/fgwh.2025.1461475  \nCOPYRIGHT  \n© 2025 Walle, Kebede, Adem and Mamo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa  \nAgmasie Damtew Walle1*, Shimels Derso Kebede2, Jibril Bashir Adem3 and Daniel Niguse Mamo4  \n1Department of Health Informatics, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Birhan, Ethiopia, 2Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia, 3Department of Public Health, College of Medicine and Health Sciences, Arsi University, Asella, Ethiopia, 4Department of Health Informatics, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia  \nBackground: Maternal and child health remains a global public health issue, particularly in low- and middle-income countries where maternal and child mortality are extremely high. The World Health Organization estimates that close to 287,000 women die annually due to pregnancy and childbirth complications, and the majority of these deaths occur where skilled birth attendants are not readily available. Reducing the prevalence of home delivery is a key strategy for lowering the maternal mortality rate. Although several studies have explored home delivery and antenatal care (ANC) utilization independently, limited evidence exists on predicting home delivery after ANC visits using machine-learning approaches in East Africa.  \nMethods: This study utilized a community-based, cross-sectional design with data from the most recent Demographic and Health Surveys conducted between 2011 and 2021 in 12 countries in East Africa countries. A total weighted sample of 44,123 women was analyzed using Python version 3 .11. Nine supervised machine-learning algorithms were applied, following Yufeng Guo’s steps for supervised learning. The random forest (RF) model, selected asthe best-performing algorithm, was used to predict home delivery after ANC visits. A SHapley Additive exPlanations analysis was conducted to identify key predictors influencing home delivery decisions.  \nResults: Home delivery after ANC visits was most prevalent in Malawi (17 . 88%), Uganda (15 .38%), and Kenya (11 .3%), and was low in Comoros (2 .38%) . Living in rural areas and late ANC initiation (second trimester) increased the likelihood of home delivery. In contrast, factors such as higher household income, husband’s level of primary and secondary education, contraceptive use, shorter birth intervals, absence of distance-related barriers to healthcare, and attending more than four ANC visits were associated with a lower likelihood of home delivery.  \nFrontiers in Global Women’s Health 01 [frontiersin.org](frontiersin.org)  \nConclusion: The study demonstrates that home delivery after AN","cbCaivgehR6Yhy9T","https://ap.wps.com/l/cbCaivgehR6Yhy9T","pdf",1485869,1,12,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What question does the study address?\",\"answer\":\"It predicts home delivery after antenatal care (ANC) visits among reproductive-age women in East Africa using machine-learning approaches.\"},{\"question\":\"Which model performed best in the prediction task?\",\"answer\":\"The random forest (RF) model was selected as the best-performing algorithm for predicting home delivery after ANC visits.\"},{\"question\":\"What factors increased or decreased the likelihood of home delivery after ANC visits?\",\"answer\":\"Home delivery was more likely with rural residence and late ANC initiation (second trimester). Higher household income, husband’s education, contraceptive use, shorter birth intervals, fewer distance-related barriers, and attending more than four ANC visits were associated with lower likelihood.\"}]","Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa | PDF",1785945091,30,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-algorithm-to-predict-home-delivery-after-antenatal-care-visit-among-reproductive-age-women-in-east-africa","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-algorithm-to-predict-home-delivery-after-antenatal-care-visit-among-reproductive-age-women-in-east-africa/128147/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"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},"What question does the study address?","Question",{"text":76,"@type":77},"It predicts home delivery after antenatal care (ANC) visits among reproductive-age women in East Africa using machine-learning approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which model performed best in the prediction task?",{"text":81,"@type":77},"The random forest (RF) model was selected as the best-performing algorithm for predicting home delivery after ANC visits.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors increased or decreased the likelihood of home delivery after ANC visits?",{"text":85,"@type":77},"Home delivery was more likely with rural residence and late ANC initiation (second trimester). Higher household income, husband’s education, contraceptive use, shorter birth intervals, fewer distance-related barriers, and attending more than four ANC visits were associated with lower likelihood.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"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":107,"slug":138},19,"General","general"]