[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122330-en":3,"doc-seo-122330-105":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},122330,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa - employing innovative machine learning techniques","Adverse birth outcomes—including preterm birth, low birth weight, and stillbirth—remain a major global health challenge, especially in developing regions. This study develops a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning. It further applies data science interpretability methods to determine key risk factors and estimate each feature’s contribution to model predictions. Findings support targeted action for high-risk groups.","Ngusie etal. BMC Public Health (2024) 24:2029 BMC Public Health  \n[https://doi.org/10.1186/s12889-024-19566-8](https://doi.org/10.1186/s12889-024-19566-8)  \nRESEARCH Open Access  \nPredicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques  \nHabtamu Setegn Ngusie 1*, Shegaw Anagaw Mengiste2, Alemu Birara Zemariam3, Bogale Mol la4, Getanew Aschalew Tesfa5, Binyam Tariku Seboka5, Tilahun Dessie Alene6 and Jing Sun7,8  \nAbstract  \nBackground Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction.  \nMethods The study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS) . We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability.  \nResults Based on our findings, about 28 . 59%(95% CI: 28 . 36, 28 . 83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88 . 0% . The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region.  \nConclusion The region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies  \n*Correspondence: Habtamu Setegn Ngusie [habtamuhi3@gmail.com](habtamuhi3@gmail.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/l](creativecommons.org/l)icenses/by-nc-nd/4.0/.  \nNgusie et al. BMC Public Health (2024)","cbCaioxfXtZJmONM","https://ap.wps.com/l/cbCaioxfXtZJmONM","pdf",2278198,1,16,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Global burden and disparities\n## Low birth weight and related outcomes\n## Regional prevalence and key indicators","[{\"question\":\"What adverse birth outcomes are considered in the study?\",\"answer\":\"The study focuses on preterm birth, low birth weight, and stillbirth as adverse birth outcomes. These outcomes are treated as the target for prediction.\"},{\"question\":\"What data and sample size are used to build the predictive model?\",\"answer\":\"The model uses Demographic Health Survey (DHS) data from women of childbearing age in 26 Sub-Saharan African countries. The total sample includes 139,659 participants who gave birth within five years before data collection.\"},{\"question\":\"Which model performed best and what key risk factors were identified?\",\"answer\":\"A random forest model achieved top performance with AUC 0.95 and accuracy 88.0%. Key risk factors included home deliveries, lack of prenatal iron supplementation, fewer than four ANC visits, short/long delivery intervals, unwanted pregnancy, primiparity, and West African region residence.\"}]","Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa - employing innovative machine learning techniques | PDF",1785810039,40,{"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},"predicting-adverse-birth-outcome-among-childbearing-women-in-sub-saharan-africa-employing-innovative-machine-learning-techniques","",{"@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/predicting-adverse-birth-outcome-among-childbearing-women-in-sub-saharan-africa-employing-innovative-machine-learning-techniques/122330/",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","2026-08-04",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 adverse birth outcomes are considered in the study?","Question",{"text":75,"@type":76},"The study focuses on preterm birth, low birth weight, and stillbirth as adverse birth outcomes. These outcomes are treated as the target for prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and sample size are used to build the predictive model?",{"text":80,"@type":76},"The model uses Demographic Health Survey (DHS) data from women of childbearing age in 26 Sub-Saharan African countries. The total sample includes 139,659 participants who gave birth within five years before data collection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what key risk factors were identified?",{"text":84,"@type":76},"A random forest model achieved top performance with AUC 0.95 and accuracy 88.0%. Key risk factors included home deliveries, lack of prenatal iron supplementation, fewer than four ANC visits, short/long delivery intervals, unwanted pregnancy, primiparity, and West African region residence.","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"]