{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:45:57Z","timestamp":1790642757422,"version":"4.1.0"},"reference-count":63,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Public Health Nutr."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S1368980021004262_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1368980021004262_as2\">\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1368980021004262_as3\">\n                    <jats:title>Setting:<\/jats:title>\n                    <jats:p>Households in Ethiopia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1368980021004262_as4\">\n                    <jats:title>Participants:<\/jats:title>\n                    <jats:p>A total of 9471 children below 5 years of age participated in this study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1368980021004262_as5\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1368980021004262_as6\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1368980021004262","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T04:01:59Z","timestamp":1633665719000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":53,"title":["Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia"],"prefix":"10.1017","author":[{"given":"Fikrewold H","family":"Bitew","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corey S","family":"Sparks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel H","family":"Nyarko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"S1368980021004262_ref6","volume-title":"Ethiopia Demographic and Health Survey 2016","year":"2016"},{"key":"S1368980021004262_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)60937-X"},{"key":"S1368980021004262_ref44","doi-asserted-by":"publisher","DOI":"10.6339\/JDS.201901_17(1).0009"},{"key":"S1368980021004262_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2015.11.036"},{"key":"S1368980021004262_ref28","first-page":"76","article-title":"WHO child growth standards based on length\/height, weight, and age","volume":"450","year":"2006","journal-title":"Acta Paediatr Suppl"},{"key":"S1368980021004262_ref48","first-page":"2328","article-title":"Key determinants of malnutrition of children under 5 years of age in Rwanda: simultaneous measurement of three anthropometric indices","volume":"30","author":"Habyarimana","year":"2016","journal-title":"Afr Popul Stud"},{"key":"S1368980021004262_ref40","doi-asserted-by":"publisher","DOI":"10.2307\/2529310"},{"key":"S1368980021004262_ref46","doi-asserted-by":"publisher","DOI":"10.35188\/UNU-WIDER\/2016\/191-8"},{"key":"S1368980021004262_ref54","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0219665"},{"key":"S1368980021004262_ref26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0208937"},{"key":"S1368980021004262_ref49","doi-asserted-by":"publisher","DOI":"10.1093\/intqhc\/mzx129"},{"key":"S1368980021004262_ref53","volume-title":"Women\u2019s Empowerment and Nutrition: An Evidence Review. 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Published by Cambridge University Press on behalf of The Nutrition Society","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}