Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali

Currently, child acute malnutrition continues to be a serious public health problem, and although its most fatal consequences are well known, its associated factors still need to be studied in more depth in different contexts. The objective of the present study is to determine the association betwee...

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Autores: Sánchez Martínez, Luis Javier, Dougnon, Abdias Ogobara, Toure, Fanta, Vargas, Antonio, Hernández, Candela Lucía, López Ejeda, Noemí
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/124344
Acceso en línea:https://hdl.handle.net/20.500.14352/124344
Access Level:acceso abierto
Palabra clave:613.24-053.2
612.391
614(1-773)
616.393
Child wasting
Determinants
Predictive algorithms
Random forest
Undernutrition
Dietética y nutrición (Medicina)
Salud pública (Medicina)
3206 Ciencias de la Nutrición
3206.10 Enfermedades de la Nutrición
3212 Salud Publica
6307.02 Países en Vías de desarrollo
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spelling Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and MaliSánchez Martínez, Luis JavierDougnon, Abdias OgobaraToure, FantaVargas, AntonioHernández, Candela LucíaLópez Ejeda, Noemí613.24-053.2612.391614(1-773)616.393Child wastingDeterminantsPredictive algorithmsRandom forestUndernutritionDietética y nutrición (Medicina)Salud pública (Medicina)3206 Ciencias de la Nutrición3206.10 Enfermedades de la Nutrición3212 Salud Publica6307.02 Países en Vías de desarrolloCurrently, child acute malnutrition continues to be a serious public health problem, and although its most fatal consequences are well known, its associated factors still need to be studied in more depth in different contexts. The objective of the present study is to determine the association between socioeconomic variables and acute malnutrition severity in rural emergency contexts of Niger and Mali. The present study consists of a secondary analysis of controlled trials. Data related to a total of 1447 treated children (6–59 months of age) were considered, for whom the Variable Selection Using Random Forests (VSURF) algorithm was applied to create interpretation and prediction random forest models (considering 86 variables). In Mali and Niger, the prediction models agree in pointing out aspects related to the water source and the work activity of caregivers as some of the main risk factors for developing severe acute malnutrition. However, the interpretation models highlight important heterogeneity, with the distance to the health center being the greatest exponent of this situation, being the most important factor in Niger while disappearing in Mali. The prediction accuracy in the interpretation model was 68.0% in Niger and 79.80% in Mali, while the prediction model reached similar rates of 63.17% and 75.63%, respectively. Machine learning techniques have proven to be a valid tool to interpret and predict the degree of severity of acute malnutrition based on socioeconomic characteristics, including complex interrelationships. The results obtained point out different aspects to be addressed to prevent and minimize the effects of acute malnutrition.John Wiley & SonsUniversidad Complutense de Madrid20252025-08-1220252025-08-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/124344reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1243442026-06-02T12:44:21Z
dc.title.none.fl_str_mv Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
title Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
spellingShingle Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
Sánchez Martínez, Luis Javier
613.24-053.2
612.391
614(1-773)
616.393
Child wasting
Determinants
Predictive algorithms
Random forest
Undernutrition
Dietética y nutrición (Medicina)
Salud pública (Medicina)
3206 Ciencias de la Nutrición
3206.10 Enfermedades de la Nutrición
3212 Salud Publica
6307.02 Países en Vías de desarrollo
title_short Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
title_full Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
title_fullStr Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
title_full_unstemmed Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
title_sort Socioeconomic Risk Factors Associated With Acute Malnutrition Severity Among Under‐Five Children Based on a Machine Learning Approach: The Case of Rural Emergency Contexts in Niger and Mali
dc.creator.none.fl_str_mv Sánchez Martínez, Luis Javier
Dougnon, Abdias Ogobara
Toure, Fanta
Vargas, Antonio
Hernández, Candela Lucía
López Ejeda, Noemí
author Sánchez Martínez, Luis Javier
author_facet Sánchez Martínez, Luis Javier
Dougnon, Abdias Ogobara
Toure, Fanta
Vargas, Antonio
Hernández, Candela Lucía
López Ejeda, Noemí
author_role author
author2 Dougnon, Abdias Ogobara
Toure, Fanta
Vargas, Antonio
Hernández, Candela Lucía
López Ejeda, Noemí
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 613.24-053.2
612.391
614(1-773)
616.393
Child wasting
Determinants
Predictive algorithms
Random forest
Undernutrition
Dietética y nutrición (Medicina)
Salud pública (Medicina)
3206 Ciencias de la Nutrición
3206.10 Enfermedades de la Nutrición
3212 Salud Publica
6307.02 Países en Vías de desarrollo
topic 613.24-053.2
612.391
614(1-773)
616.393
Child wasting
Determinants
Predictive algorithms
Random forest
Undernutrition
Dietética y nutrición (Medicina)
Salud pública (Medicina)
3206 Ciencias de la Nutrición
3206.10 Enfermedades de la Nutrición
3212 Salud Publica
6307.02 Países en Vías de desarrollo
description Currently, child acute malnutrition continues to be a serious public health problem, and although its most fatal consequences are well known, its associated factors still need to be studied in more depth in different contexts. The objective of the present study is to determine the association between socioeconomic variables and acute malnutrition severity in rural emergency contexts of Niger and Mali. The present study consists of a secondary analysis of controlled trials. Data related to a total of 1447 treated children (6–59 months of age) were considered, for whom the Variable Selection Using Random Forests (VSURF) algorithm was applied to create interpretation and prediction random forest models (considering 86 variables). In Mali and Niger, the prediction models agree in pointing out aspects related to the water source and the work activity of caregivers as some of the main risk factors for developing severe acute malnutrition. However, the interpretation models highlight important heterogeneity, with the distance to the health center being the greatest exponent of this situation, being the most important factor in Niger while disappearing in Mali. The prediction accuracy in the interpretation model was 68.0% in Niger and 79.80% in Mali, while the prediction model reached similar rates of 63.17% and 75.63%, respectively. Machine learning techniques have proven to be a valid tool to interpret and predict the degree of severity of acute malnutrition based on socioeconomic characteristics, including complex interrelationships. The results obtained point out different aspects to be addressed to prevent and minimize the effects of acute malnutrition.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-12
2025
2025-08-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/124344
url https://hdl.handle.net/20.500.14352/124344
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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