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DHS data from 2019 and 2021 are combined for 35,460 women aged 15 and above. Feature selection uses chi-square, f-classifiers, recursive feature elimination, and mutual information, yielding 19 features. Random forest, bagged decision tree, cat boost, and extra tree are evaluated. The chi-square method achieves 92.82% accuracy, while extra-tree attains 91.03% predictive accuracy and identifies key predictors including age, wealth index, BMI, education, and media or mosquito-net exposure.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multi-class-machine-learning-classification-and-important-features-on-anemia-among-women-in-tanzania-and-rwanda/407023/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multi-class-machine-learning-classification-and-important-features-on-anemia-among-women-in-tanzania-and-rwanda/407023.png","ImageObject",300,407,{"name":92,"@type":93},"LangkahRina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s goal regarding anemia among women in Tanzania and Rwanda?","Question",{"text":112,"@type":113},"To predict the anemia status and identify important features associated with anemia among women in the two countries.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the dataset constructed and what sample size was used?",{"text":117,"@type":113},"Tanzania and Rwanda DHS data from 2019 and 2021 were merged, using data for 35,460 women aged 15 years and above.",{"name":119,"@type":110,"acceptedAnswer":120},"Which feature selection method and machine learning model performed best?",{"text":121,"@type":113},"Chi-square was the most effective feature selection method with 92.82% accuracy, and the extra-tree algorithm achieved the best predictive accuracy of 91.03%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},407023,1791588310,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962090893776,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Kebede etal. BMC Public Health (2025) 25:4382 BMC Public Health  \n[https://doi.org/10.1186/s12889-025-25560-5](https://doi.org/10.1186/s12889-025-25560-5)  \nRESEARCH Open Access  \nMulti-class machine learning classification and important features on anemia among women in Tanzania and Rwanda  \nSefinew Gebeyehu Kebede 1*, Wasihun Kindalem Worku2, Gizachew Mul u Setegn2 andYeshambel KinduYihuna 1  \nAbstract  \nIntroduction Anemia is a reduction in the number of hemoglobin levels in the blood that has significant adverse health consequences. It is a public health problem among women’s reproductive age, affecting both poor and rich countries overall the world. That is the study aimed to predict the status and identify the important features of anemia among women in Tanzania and Rwanda.  \nMehods We merged Tanzania and Rwanda data from DHS conducted in 2019 and 2021 and worked with 35,460 women 15 years and above. Feature selection was performed with the help of chi-square, f-classifiers, recursive feature elimination, and mutual information methods. The best performing one was applied to consider 19 features. Four machine learning algorithms—random forest, bagged decision tree, cat boost, and extra tree—were applied to predict and classify the anemia status.  \nResult The chi-square method was utilized most effectively in feature selection, with accuracy of 92 . 82% . The research revealed a high prevalence of anemia in both countries. Out of the women in Tanzania, 2% had severe anemia, 11% had moderate anemia, and 10% had mild anemia. Among women in Rwanda, 4% had any anemia, including 2% mild anemia in particular. Among the machine learning algorithms attempted, the extra-tree algorithm performed with the best predictive accuracy, which was 91. 03% . However, the cat boost model performed with the worst accuracy of 79. 21% .  \nConclusion The extra-tree model has a stronger predictive capability than other machine learning models among the four predictive models constructed using these methods. The extra tree model result indicates that among fertile women, the most significant predictors of anemia were age, wealth index, BMI, education level, television watching, mosquito bed net use, and radio listening. The identification of such high-risk countries could provide useful information to decision-makers trying to reduce anemic women.  \nKeywords Anemia, Women, Machine learning, Features  \n*Correspondence:  \nSefinew Gebeyehu Kebede  \n[sefinew009@gmail.com](sefinew009@gmail.com)  \n1Department of Biostatistics, Debark University, Debark, Amhara, Ethiopia 2Department of Computer Science, Debark University, Debark, Amhara, Ethiopia  \n© The Author(s) 2025. 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://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nKebede et al. BMC Public Health (2025) 25:4382  \nIntroduction  \nAnemia is characterized by a decrease in hemoglobin levels in the bloodstream, which can lead to significant health issues [1]. It poses a public health challenge for women of reproduc","cbCainbQL3SaMTH3","https://ap.wps.com/l/cbCainbQL3SaMTH3","pdf",2058968,14,"English","# Abstract\n# Introduction\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What is the study’s goal regarding anemia among women in Tanzania and Rwanda?\",\"answer\":\"To predict the anemia status and identify important features associated with anemia among women in the two countries.\"},{\"question\":\"How was the dataset constructed and what sample size was used?\",\"answer\":\"Tanzania and Rwanda DHS data from 2019 and 2021 were merged, using data for 35,460 women aged 15 years and above.\"},{\"question\":\"Which feature selection method and machine learning model performed best?\",\"answer\":\"Chi-square was the most effective feature selection method with 92.82% accuracy, and the extra-tree algorithm achieved the best predictive accuracy of 91.03%.\"}]","Multi-class machine learning classification and important features on anemia among women in Tanzania and Rwanda | PDF",1790569554,35]