Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies.

ABSTRACT: Machine learning is a class of algorithms able to handle a large number of predictors with potentially nonlinear relationships. By applying machine learning to obesity, researchers can examine how risk factors across multiple settings (e.g., school and home) interact to best predict childhood obesity risk. In this narrative review, we provide an overview of studies that have applied machine learning to predict childhood obesity using a combination of sociodemographic and behavioral risk factors. The objective is to summarize the key determinants of obesity identified in existing machine learning studies and highlight opportunities for future machine learning applications in the field. Of 15 peer-reviewed studies, approximately half examined early childhood (0-24 months of age) deter

SUBMITTER: LeCroy MN 

PROVIDER: S-EPMC8418446 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature