Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

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. This study draws on data from the Ethiopian Demographic and Health Survey o...

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Vydané v:	Public health nutrition Ročník 25; číslo 2; s. 269 - 280
Hlavní autori:	Bitew, Fikrewold H, Sparks, Corey S, Nyarko, Samuel H
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge, UK Cambridge University Press 01.02.2022
Predmet:	Age Algorithms Anemia Artificial intelligence birth weight Child Child, Preschool childhood Children Children & youth Classification Demographics Discriminant analysis Ethiopia Ethiopia - epidemiology Fertility Food security Growth Disorders - etiology Health surveys Households Humans Infant Learning algorithms Machine Learning Malnutrition Malnutrition - complications Malnutrition - diagnosis Malnutrition - epidemiology Maternal & child health Neural networks Nutrition Nutritional status Nutritional Status and Body Composition prediction Predictions Prevalence Public health Research Paper Risk analysis Risk factors Thinness - epidemiology Thinness - etiology Undernutrition Underweight Variables Water supply Ethiopia Africa Determinants Predictive algorithms Child undernutrition Spatial variations Ethiopia Machine learning
ISSN:	1368-9800, 1475-2727, 1475-2727
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Abstract	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. 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. Households in Ethiopia. A total of 9471 children below 5 years of age participated in this study. 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. 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.
AbstractList	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.OBJECTIVEChild 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.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.DESIGNThis 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.Households in Ethiopia.SETTINGHouseholds in Ethiopia.A total of 9471 children below 5 years of age participated in this study.PARTICIPANTSA total of 9471 children below 5 years of age participated in this study.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.RESULTSThe 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.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.CONCLUSIONSThe 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. 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. 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. Households in Ethiopia. A total of 9471 children below 5 years of age participated in this study. 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. 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. Objective: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.Design: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.Setting:Households in Ethiopia.Participants:A total of 9471 children below 5 years of age participated in this study.Results: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.Conclusions: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. 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. 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. Households in Ethiopia. A total of 9471 children below 5 years of age participated in this study. 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. 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.
Author	Sparks, Corey S Bitew, Fikrewold H Nyarko, Samuel H
AuthorAffiliation	Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio , 9947 Bricewood Hill, San Antonio , TX 78254 , USA
AuthorAffiliation_xml	– name: Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio , 9947 Bricewood Hill, San Antonio , TX 78254 , USA
Author_xml	– sequence: 1 givenname: Fikrewold H surname: Bitew fullname: Bitew, Fikrewold H email: fikre.wold@gmail.com organization: Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio, 9947 Bricewood Hill, San Antonio, TX 78254, USA – sequence: 2 givenname: Corey S surname: Sparks fullname: Sparks, Corey S organization: Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio, 9947 Bricewood Hill, San Antonio, TX 78254, USA – sequence: 3 givenname: Samuel H surname: Nyarko fullname: Nyarko, Samuel H organization: Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio, 9947 Bricewood Hill, San Antonio, TX 78254, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34620263$$D View this record in MEDLINE/PubMed
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Copyright	The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society. 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. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at: https://uk.sagepub.com/en-gb/eur/reusing-open-access-and-sage-choice-content The Authors 2021 2021 The Authors
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Keywords	Determinants Predictive algorithms Child undernutrition Spatial variations Ethiopia Machine learning
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Snippet	Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of... Objective:Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the...
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SubjectTerms	Age Algorithms Anemia Artificial intelligence birth weight Child Child, Preschool childhood Children Children & youth Classification Demographics Discriminant analysis Ethiopia Ethiopia - epidemiology Fertility Food security Growth Disorders - etiology Health surveys Households Humans Infant Learning algorithms Machine Learning Malnutrition Malnutrition - complications Malnutrition - diagnosis Malnutrition - epidemiology Maternal & child health Neural networks Nutrition Nutritional status Nutritional Status and Body Composition prediction Predictions Prevalence Public health Research Paper Risk analysis Risk factors Thinness - epidemiology Thinness - etiology Undernutrition Underweight Variables Water supply
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Title	Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia
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