[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290930-105":59,"doc-detail-290930-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","prediction-of-stunting-among-under-5-children-in-rwanda-using-machine-learning-techniques","Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques","","Research focuses on early detection of stunting in Rwanda, where stunting remains a health and policy challenge despite declines in recent years. Using Rwanda Demographic and Health Survey 2019–2020 secondary data, stratified 10-fold cross-validation trains and evaluates multiple machine learning classifiers for stunting status prediction. Results identify gradient boosting as the best model, achieving test performance with sensitivity, specificity, F1, and an AUC of 0.89. Key predictors include mother’s height, television exposure, child age, province, maternal education, birth weight, and childbirth size.",{"@graph":69,"@context":126},[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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prediction-of-stunting-among-under-5-children-in-rwanda-using-machine-learning-techniques/290930/",{"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/prediction-of-stunting-among-under-5-children-in-rwanda-using-machine-learning-techniques/290930.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What was the goal of the study on stunting in Rwanda?","Question",{"text":112,"@type":113},"To develop a machine learning model that predicts stunting status among Rwandan children under five for early detection and support of prevention and control efforts.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and evaluation approach were used to build the prediction models?",{"text":117,"@type":113},"The study used Rwanda Demographic and Health Survey 2019–2020 secondary data, applying stratified 10-fold cross-validation to train classifiers and compare models using multiple metrics.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best, and how well did it classify stunting?",{"text":121,"@type":113},"Gradient boosting produced the best model, with test classification performance reporting accuracy and related measures, including sensitivity and specificity, and an area under the curve (AUC) of 0.89.",{"name":123,"@type":110,"acceptedAnswer":124},"What predictors were identified as most important for stunting status?",{"text":125,"@type":113},"Mother’s height, television exposure, child’s age, province, mother’s education, birth weight, and childbirth size were reported as the most important predictors.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},290930,1789642975,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":133,"read_time":147,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Original Article  \nJ Prev Med Public Health 2023;56:41-49 • [https://doi.org/10.3961/jpmph.22.388](https://doi.org/10.3961/jpmph.22.388)  \nJournal of  Preventive Medicine & Public Health  \npISSN 1975-8375 eISSN 2233-4521  \nPrediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques  \nSimilien Ndagijimana1, Ignace Habimana Kabano1, Emmanuel Masabo1, Jean Marie Ntaganda2  \n1African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda; 2University of Rwanda College of Science and Technology, Kigali, Rwanda  \nObjectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be madea community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.  \nMethods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.  \nResults: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.  \nConclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.  \nKey words: Machine learning, Prediction, Under-5 children, Stunting, Rwanda  \nINTRODUCTION  \nIn 2020, 149.2 million under-5 children were stunted globally, and Africa accounted for 40% of them [1] . Although the global number of stunted children has decreased considerably  \nReceived: August 30, 2022 Accepted: December 15, 2022  \nCorresponding author: Similien Ndagijimana  \nAfrican Centre of Excellence in Data Science, University of Rwanda, P. O Box 4285 Kigali, Rwanda  \n[E-mail:](E-mail: similienn@gmail.com)[ similienn@gmail.com](E-mail: similienn@gmail.com)  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([https://creativecommons.org/licenses/by](https://creativecommons.org/licenses/by)nc/4 . 0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nsince 1990, Africa is the only region where the number of stunted children has increased despite a drop in the prevalence of stunting [2,3]. Stunting in children under the age of 5 remains an issue in Rwanda, despite the government’s tremendous success in reducing infant mortality. Stunting in children under the age of 5 has decreased significantly over the last 15 years, according to the Rwanda Demographic and Health Survey (RDHS), with the prevalence of stunting decreasing from 51% in 2005 to 38% in 2015 and 33% in the most recent RDHS 2019- 2020 reports; however, the prevalence of stunting remains high [4].  \nEducational level, maternal demographics, nutrition and health condition, family size, and assets have all been shown  \nCopyright © 2023 The Korean Society for Preventive Medicine  \n41  \nSimilien Ndagijimana, et al. ","cbCaifxwYYgsXT0D","https://ap.wps.com/l/cbCaifxwYYgsXT0D","pdf",443298,"English","# Objectives\n# Methods\n# Results\n# Conclusions\n# Key words\n## Introduction","[{\"question\":\"What was the goal of the study on stunting in Rwanda?\",\"answer\":\"To develop a machine learning model that predicts stunting status among Rwandan children under five for early detection and support of prevention and control efforts.\"},{\"question\":\"What data and evaluation approach were used to build the prediction models?\",\"answer\":\"The study used Rwanda Demographic and Health Survey 2019–2020 secondary data, applying stratified 10-fold cross-validation to train classifiers and compare models using multiple metrics.\"},{\"question\":\"Which model performed best, and how well did it classify stunting?\",\"answer\":\"Gradient boosting produced the best model, with test classification performance reporting accuracy and related measures, including sensitivity and specificity, and an area under the curve (AUC) of 0.89.\"},{\"question\":\"What predictors were identified as most important for stunting status?\",\"answer\":\"Mother’s height, television exposure, child’s age, province, mother’s education, birth weight, and childbirth size were reported as the most important predictors.\"}]","Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques | PDF",23]