[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125067-en":3,"doc-seo-125067-105":30,"detail-sidebar-cat-0-en-105":95},{"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},125067,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting place of delivery choice among childbearing women in East Africa - a comparative analysis of advanced machine learning techniques","Sub-Saharan Africa faces high neonatal and maternal mortality linked to limited access to skilled care during childbirth. This study examines delivery-location decisions among 86,009 childbearing women across East Africa, improving classification of health facilities and home deliveries. A comparative evaluation of 12 advanced machine learning algorithms used data-balancing and hyperparameter optimization. Results show health-facility delivery prevalence at 83.71%, with SVM and CatBoost achieving the highest predictive performance. Association rule mining highlights education, early ANC timing, wealth, marital status, phone ownership, religion, media access, and birth order as key correlates.","TYPE Original Research PUBLISHED 27 November 2024 DOI 10. 3389/fpubh.2024.1439320  \nOPEN ACCESS  \nEDITED BY  \nMd. Akhtarul Islam,  \nKhulna University, Bangladesh  \nREVIEWED BY  \nUmesh Ghimire,  \nIndiana University, United States Ricardo Valentim,  \nFederal University of Rio Grande do Norte, Brazil  \n*CORRESPONDENCE  \nHabtamu Setegn Ngusie  \n [habtamuhi3@gmail.com](habtamuhi3@gmail.com)  \nRECEIVED 27 May 2024  \nACCEPTED 11 November 2024  \nPUBLISHED 27 November 2024  \nCITATION  \nNgusie HS, Tesfa GA, Taddese AA, Enyew EB, Alene TD, Abebe GK, Walle AD and Zemariam AB (2024) Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.  \nFront. Public Health 12:1439320 .  \ndoi: 10.3389/fpubh.2024.1439320  \nCOPYRIGHT  \n© 2024 Ngusie, Tesfa, Taddese, Enyew, Alene, Abebe, Walle and Zemariam. 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.  \nPredicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques  \nHabtamu Setegn Ngusie1*, Getanew Aschalew Tesfa2 , Asefa Adimasu Taddese3 , Ermias Bekele Enyew4 , Tilahun Dessie Alene5 , Gebremeskel Kibret Abebe6 , Agmasie Damtew Walle7 and Alemu Birara Zemariam8  \n1 Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia, 2 School of Public Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia, 3 Department of Sport, Physical Education and Health (SPEH), Academy of Wellness and Human Development, Faculty of Arts and Social Sciences, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China, 4 Department of Health Informatics, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia, 5 Department of Pediatric and Child Health, School of Medicine, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia,  \n6 Department of Emergency and Critical Care Nursing, School of Nursing, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia, 7 Department of Health Informatics, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia, 8 Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia  \nBackground: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women’s choices of delivery locations in East Africa.  \nMethod: The study focused on 86,009 childbearing women in East Africa. A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.  \nResult: The prevalence of health facility delivery in East Africa was found to be 83. 71% . The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant di􀀀erence between them in all comprehensive analysis of metrics performance. Factors associated with facility-based deliveries were identified using association rule mining, including parental education levels, ","cbCaiaIIhPojLkG2","https://ap.wps.com/l/cbCaiaIIhPojLkG2","pdf",2259665,1,17,"English","en",105,"# Background\n## Research aim\n# Method\n## Study population and algorithms\n## Model optimization and evaluation\n## Data balancing\n# Result\n## Prevalence of health-facility delivery\n## Best-performing machine learning models\n## Key associated factors\n# Conclusion\n## Intervention recommendations\n## Future research directions","[{\"question\":\"What problem does the study address regarding childbirth in East Africa?\",\"answer\":\"Limited access to skilled healthcare during delivery contributes to high neonatal and maternal mortality in Sub-Saharan Africa. The study focuses on improving delivery-location classification and understanding drivers of women’s choices.\"},{\"question\":\"How was the prediction model developed and evaluated?\",\"answer\":\"The study analyzed data from 86,009 childbearing women and compared 12 advanced machine learning algorithms. It applied data balancing and hyperparameter optimization, including Bayesian tuning, to enhance performance and evaluate metrics.\"},{\"question\":\"Which factors were identified as associated with facility-based delivery?\",\"answer\":\"Association rule mining indicated that parental education, timing of initial ANC check-ups, wealth status, marital status, mobile phone ownership, religious affiliation, media accessibility, and birth order are associated with facility-based deliveries.\"},{\"question\":\"What model performance results did the study report?\",\"answer\":\"Health-facility delivery prevalence was 83.71%. The SVM algorithm and CatBoost performed best, reaching about 95% accuracy and an AUC of 0.98 after optimization, with no significant difference between them across comprehensive metric analyses.\"}]","Predicting place of delivery choice among childbearing women in East Africa - a comparative analysis of advanced machine learning techniques | PDF",1785896446,43,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"predicting-place-of-delivery-choice-among-childbearing-women-in-east-africa-a-comparative-analysis-of-advanced-machine-learning-techniques","",{"@graph":36,"@context":89},[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-place-of-delivery-choice-among-childbearing-women-in-east-africa-a-comparative-analysis-of-advanced-machine-learning-techniques/125067/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address regarding childbirth in East Africa?","Question",{"text":75,"@type":76},"Limited access to skilled healthcare during delivery contributes to high neonatal and maternal mortality in Sub-Saharan Africa. The study focuses on improving delivery-location classification and understanding drivers of women’s choices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the prediction model developed and evaluated?",{"text":80,"@type":76},"The study analyzed data from 86,009 childbearing women and compared 12 advanced machine learning algorithms. It applied data balancing and hyperparameter optimization, including Bayesian tuning, to enhance performance and evaluate metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as associated with facility-based delivery?",{"text":84,"@type":76},"Association rule mining indicated that parental education, timing of initial ANC check-ups, wealth status, marital status, mobile phone ownership, religious affiliation, media accessibility, and birth order are associated with facility-based deliveries.",{"name":86,"@type":73,"acceptedAnswer":87},"What model performance results did the study report?",{"text":88,"@type":76},"Health-facility delivery prevalence was 83.71%. 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