[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122518-en":3,"doc-seo-122518-105":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},122518,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Exploring machine learning classification for community based health insurance enrollment in Ethiopia","Community-based health insurance (CBHI) supports universal health coverage by improving access to care and reducing financial hardship from illness costs. This study used 2019 Ethiopia Mini Demographic and Health Survey data to compare seven machine learning classifiers for predicting CBHI enrollment and to evaluate model performance using receiver operating characteristic curves and additional accuracy metrics. Random forest achieved the strongest predictive accuracy, while age, wealth index, household members, and land utilization emerged as key influential predictors. Results inform targeted strategies for higher CBHI uptake.","TYPE Original Research PUBLISHED 18 July 2025  \nDOI 10.3389/fpubh.2025.1549210  \nOPEN ACCESS  \nEDITED BY  \nDawit Getnet Ayele,  \nDistrict of Columbia Department of Health, United States  \nREVIEWED BY  \nAlexandre Morais Nunes, University of Lisbon, Portugal Yawkal Tsega,  \nWollo University, Ethiopia  \n*CORRESPONDENCE  \nSeyifemickael Amare Yilema  \n [samarey1981@gmail.com](samarey1981@gmail.com)  \nRECEIVED 20 December 2024  \nACCEPTED 24 June 2025  \nPUBLISHED 18 July 2025  \nCITATION  \nYilema SA, Shiferaw YA, Moyehodie YA, Fenta SM, Belay DB, Fenta HM,  \nNigussie TZ and Chen D-G (2025) Exploring machine learning classification for community based health insurance enrollment in Ethiopia.  \nFront. Public Health 13:1549210 .  \ndoi: 10.3389/fpubh.2025.1549210  \nCOPYRIGHT  \n© 2025 Yilema, Shiferaw, Moyehodie, Fenta, Belay, Fenta, Nigussie and Chen. 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.  \nExploring machine learning classification for community based health insurance enrollment in Ethiopia  \nSeyifemickael Amare Yilema1,2*, Yegnanew A. Shiferaw3, Yikeber Abebaw Moyehodie 1, Setegn Muche Fenta 1, Denekew Bitew Belay 2,4, Haile Mekonnen Fenta4,5, Teshager Zerihun Nigussie 1 and Ding-Geng Chen 2,6  \n1 Department of Statistics, Debre Tabor University, Debre Tabor, Ethiopia, 2 Department of Statistics, University of Pretoria, Pretoria, South Africa, 3 Department of Statistics, University of Johannesburg, Johannesburg, South Africa, 4 Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia, 5Center for Environmental and Respiratory Health Research (CERH), Research Unit of Population Health, University of Oulu, Oulu, Finland, 6College of Health Solutions, Arizona State University, Phoenix, AZ, United States  \nBackground: Community-based health insurance (CBHI) is a vital tool for achieving universal health coverage (UHC), a key global health priority outlined in the sustainable development goals (SDGs) . Sub-Saharan Africa continues to face challenges in achieving UHC and protecting individuals from the financial burden of disease. As a result, CBHI has become popular in low-and middleincome countries, including Ethiopia. Therefore, this study aimed to identify the ML algorithm with the best predictive accuracy for CBHI enrollment and to determine the most influential predictors among the dataset.  \nMethods: The 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. The CBHI were predicted using seven machine learning models: linear discriminant analysis (LDA), support vector machine with radial basis function (SVM), k-nearest neighbors (KNN), classification and regression tree (CART), and random forest (RF) . Receiver operating characteristic curvesand other metrics were used to evaluate each model’s accuracy.  \nResults: The RF algorithm was determined to be the best machine learning model based on different performance assessments. The result indicates that age, wealth index, household members, and land usage all significantly affect CBHI in Ethiopia.  \nConclusion: This study found that RF machine learning models could improve the ability to classify CBHI in Ethiopia with high accuracy. Age, wealth index, household members, and land utilization are some of the most significant variables associated with CBHI that were determined by feature importance. The results of the study can help health professionals and policymakers create focused strategies to improve CBHI enrollment in Ethiopia.  \nKEYWORDS  \nmachine learning, health insurance, random forest, accuracy, Ethiopia  \nFrontiers in Public ","cbCaiu7LKLNxhPyB","https://ap.wps.com/l/cbCaiu7LKLNxhPyB","pdf",738808,1,10,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To identify the machine learning algorithm with the best predictive accuracy for community-based health insurance (CBHI) enrollment and to determine the most influential predictors in the dataset.\"},{\"question\":\"Which data source and models were used?\",\"answer\":\"The study used the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data and compared seven machine learning models, including LDA, SVM, KNN, CART, and random forest (RF).\"},{\"question\":\"Why is random forest considered the best model and what predictors matter most?\",\"answer\":\"Random forest showed the best performance across multiple assessments. Age, wealth index, household members, and land utilization significantly affect CBHI enrollment in Ethiopia, indicating high feature importance.\"}]","Exploring machine learning classification for community based health insurance enrollment in Ethiopia | PDF",1785811051,25,{"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},"exploring-machine-learning-classification-for-community-based-health-insurance-enrollment-in-ethiopia","",{"@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/exploring-machine-learning-classification-for-community-based-health-insurance-enrollment-in-ethiopia/122518/",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 is the main goal of the study?","Question",{"text":75,"@type":76},"To identify the machine learning algorithm with the best predictive accuracy for community-based health insurance (CBHI) enrollment and to determine the most influential predictors in the dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data source and models were used?",{"text":80,"@type":76},"The study used the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data and compared seven machine learning models, including LDA, SVM, KNN, CART, and random forest (RF).",{"name":82,"@type":73,"acceptedAnswer":83},"Why is random forest considered the best model and what predictors matter most?",{"text":84,"@type":76},"Random forest showed the best performance across multiple assessments. 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