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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 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Marcos-Pasero H,Colmenarejo G,Aguilar-Aguilar E,Ramírez de Molina A,Reglero G,Loria-Kohen V

doi

10.1038/s41598-021-81205-8

subject

Has Abstract

pub_date

2021-01-21 00:00:00

pages

1910

issue

1

issn

2045-2322

pii

10.1038/s41598-021-81205-8

journal_volume

11

pub_type

杂志文章

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