Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

Abstract The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and ph...

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Autores principales:	Helena Marcos-Pasero, Gonzalo Colmenarejo, Elena Aguilar-Aguilar, Ana Ramírez de Molina, Guillermo Reglero, Viviana Loria-Kohen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3137e6569f2743b28bb18e71bab182c5
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spelling	oai:doaj.org-article:3137e6569f2743b28bb18e71bab182c52021-12-02T10:49:29ZRanking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques10.1038/s41598-021-81205-82045-2322https://doaj.org/article/3137e6569f2743b28bb18e71bab182c52021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81205-8https://doaj.org/toc/2045-2322Abstract The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.Helena Marcos-PaseroGonzalo ColmenarejoElena Aguilar-AguilarAna Ramírez de MolinaGuillermo RegleroViviana Loria-KohenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Helena Marcos-Pasero Gonzalo Colmenarejo Elena Aguilar-Aguilar Ana Ramírez de Molina Guillermo Reglero Viviana Loria-Kohen Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
description	Abstract The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.
format	article
author	Helena Marcos-Pasero Gonzalo Colmenarejo Elena Aguilar-Aguilar Ana Ramírez de Molina Guillermo Reglero Viviana Loria-Kohen
author_facet	Helena Marcos-Pasero Gonzalo Colmenarejo Elena Aguilar-Aguilar Ana Ramírez de Molina Guillermo Reglero Viviana Loria-Kohen
author_sort	Helena Marcos-Pasero
title	Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
title_short	Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
title_full	Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
title_fullStr	Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
title_full_unstemmed	Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
title_sort	ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3137e6569f2743b28bb18e71bab182c5
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Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

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