Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.

<h4>Aims</h4>Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML)...

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Autores principales:	S M Jubaidur Rahman, N A M Faisal Ahmed, Md Menhazul Abedin, Benojir Ahammed, Mohammad Ali, Md Jahanur Rahman, Md Maniruzzaman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7721b46d18464663b51a15af57c4c0ec
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spelling	oai:doaj.org-article:7721b46d18464663b51a15af57c4c0ec2021-12-02T20:10:28ZInvestigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.1932-620310.1371/journal.pone.0253172https://doaj.org/article/7721b46d18464663b51a15af57c4c0ec2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253172https://doaj.org/toc/1932-6203<h4>Aims</h4>Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction.<h4>Methods</h4>This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy.<h4>Results</h4>The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight.<h4>Conclusion</h4>This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh's U5 children.S M Jubaidur RahmanN A M Faisal AhmedMd Menhazul AbedinBenojir AhammedMohammad AliMd Jahanur RahmanMd ManiruzzamanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253172 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q S M Jubaidur Rahman N A M Faisal Ahmed Md Menhazul Abedin Benojir Ahammed Mohammad Ali Md Jahanur Rahman Md Maniruzzaman Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
description	<h4>Aims</h4>Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction.<h4>Methods</h4>This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy.<h4>Results</h4>The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight.<h4>Conclusion</h4>This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh's U5 children.
format	article
author	S M Jubaidur Rahman N A M Faisal Ahmed Md Menhazul Abedin Benojir Ahammed Mohammad Ali Md Jahanur Rahman Md Maniruzzaman
author_facet	S M Jubaidur Rahman N A M Faisal Ahmed Md Menhazul Abedin Benojir Ahammed Mohammad Ali Md Jahanur Rahman Md Maniruzzaman
author_sort	S M Jubaidur Rahman
title	Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
title_short	Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
title_full	Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
title_fullStr	Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
title_full_unstemmed	Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
title_sort	investigate the risk factors of stunting, wasting, and underweight among under-five bangladeshi children and its prediction based on machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7721b46d18464663b51a15af57c4c0ec
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Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.

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