[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122065-en":3,"doc-seo-122065-105":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":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},122065,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa","Acute respiratory infections remain a leading cause of death among children under five, and timely prediction is critical for prevention. This study predicts acute respiratory tract infection and identifies determinants using the most recent DHS dataset from 36 Sub-Saharan African countries (2005–2022). Five supervised machine learning models—Random Forest, Decision Tree, XGBoost, Logistic Regression, and Naive Bayes—were trained with Python and evaluated using accuracy, precision, recall, and AUC. Random Forest achieved the strongest overall performance (accuracy 96.40%, recall 78%, ROC 94%), while determinants included breastfeeding, vaccination history, media exposure, no diarrhea in the past two weeks, and birth in a health facility.","TYPE Original Research PUBLISHED 20 November 2024 DOI 10.3389/fped.2024.1388820  \nEDITED BY  \nZikria Saleem,  \nBahauddin Zakariya University, Pakistan  \nREVIEWED BY  \nClemax Couto Sant’Anna,  \nFederal University of Rio de Janeiro, Brazil Areesha Rehman,  \nBahauddin Zakariya University, Pakistan  \n*CORRESPONDENCE  \nTirualem Zeleke Yehuala  \n [sarazeleke3@gmail.com](sarazeleke3@gmail.com)  \nRECEIVED 20 February 2024  \nACCEPTED 31 October 2024  \nPUBLISHED 20 November 2024  \nCITATION  \nYehuala TZ, Fente BM, Wubante SM and Derseh NM (2024) Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa. Front. Pediatr. 12:1388820 .  \ndoi: 10.3389/fped.2024.1388820  \nCOPYRIGHT  \n© 2024 Yehuala, Fente, Wubante and Derseh. 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 algorithms to predict acute respiratory tract infection and identify its determinants among children under five in  \nSub-Saharan Africa  \nTirualem Zeleke Yehuala1*, Bezawit Melak Fente2, Sisay Maru Wubante1 and Nebiyu Mekonnen Derseh3  \n1Department Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia, 2Department of General Midwifery, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia, 3Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia  \nBackground: The primary cause of death for children under the age of five is acute respiratory infections (ARI) . Early predicting acute respiratory tract infections (ARI) and identifying their predictors using supervised machine learning algorithms is the most effective way to save the lives of millions of children. Hence, this study aimed to predict acute respiratory tract infections (ARI) and identify their determinants using the current state-of-the-art machine learning models.  \nMethods: We used the most recent demographic and health survey (DHS) dataset from 36 Sub-Saharan African countries collected between 2005 and 2022. Python software was used for data processing and machine learning model building. We employed five machine learning algorithms, such as Random Forest, Decision Tree (DT), XGBoost, Logistic Regression (LR), and Naive Bayes, to analyze risk factors associated with ARI and predict ARI in children. We evaluated the predictive models’ performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve. Result: In this study, 75,827 children under five were used in the final analysis. Among the proposed machine learning models, random forest performed best overall in the proposed classifier, with an accuracy of 96.40%, precision of 87.9%, F-measure of 82 .8%, ROC curve of 94%, and recall of 78% . Naïve Bayes accuracy has also achieved the least classification with accuracy (87.53%), precision (67%), F-score (48%), ROC curve (82%), and recall (53%) . The most significant determinants of preventing acute respiratory tract infection among under five children were having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility. These were associated positively with the outcome variable.  \nFrontiers in Pediatrics 01 [frontiersin.org](frontiersin.org)  \nConclusion: According to this study, children who didn’t take vaccinations had weakened immune systems and were h","cbCailJHf3AemD6I","https://ap.wps.com/l/cbCailJHf3AemD6I","pdf",1976552,1,11,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"Which machine learning algorithms were used to predict acute respiratory tract infection in children under five?\",\"answer\":\"The study used Random Forest, Decision Tree, XGBoost, Logistic Regression, and Naive Bayes. Models were built using Python and evaluated with standard predictive metrics.\"},{\"question\":\"How did Random Forest perform compared with the other models?\",\"answer\":\"Random Forest performed best overall, reaching 96.40% accuracy and an ROC curve of 94%, with 87.9% precision and 78% recall. Naive Bayes showed the lowest performance among the compared models.\"},{\"question\":\"What determinants were associated with preventing acute respiratory tract infection?\",\"answer\":\"Having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility were identified as significant determinants. These factors were positively associated with the outcome variable in the study.\"}]","Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa | PDF",1785808656,28,{"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-algorithms-to-predict-acute-respiratory-tract-infection-and-identify-its-determinants-among-children-under-five-in-sub-saharan-africa","",{"@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-algorithms-to-predict-acute-respiratory-tract-infection-and-identify-its-determinants-among-children-under-five-in-sub-saharan-africa/122065/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms were used to predict acute respiratory tract infection in children under five?","Question",{"text":75,"@type":76},"The study used Random Forest, Decision Tree, XGBoost, Logistic Regression, and Naive Bayes. Models were built using Python and evaluated with standard predictive metrics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did Random Forest perform compared with the other models?",{"text":80,"@type":76},"Random Forest performed best overall, reaching 96.40% accuracy and an ROC curve of 94%, with 87.9% precision and 78% recall. Naive Bayes showed the lowest performance among the compared models.",{"name":82,"@type":73,"acceptedAnswer":83},"What determinants were associated with preventing acute respiratory tract infection?",{"text":84,"@type":76},"Having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility were identified as significant determinants. 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