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

SUBMITTER: Marcos-Pasero H 

PROVIDER: S-EPMC7820584 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature