  Childhood obesity is a major public health concern. Multidisciplinary
pediatric weight management programs are considered standard treatment for
children with obesity and severe obesity who are not able to be successfully
managed in the primary care setting; however, high drop-out rates (referred to
as attrition) are a major hurdle in delivering successful interventions.
Predicting attrition patterns can help providers reduce the attrition rates.
Previous work has mainly focused on finding static predictors of attrition
using statistical analysis methods. In this study, we present a machine
learning model to predict (a) the likelihood of attrition, and (b) the change
in body-mass index (BMI) percentile of children, at different time points after
joining a weight management program. We use a five-year dataset containing the
information related to around 4,550 children that we have compiled using data
from the Nemours Pediatric Weight Management program. Our models show strong
prediction performance as determined by high AUROC scores across different
tasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predicting
weight outcomes). Additionally, we report the top features predicting attrition
and weight outcomes in a series of explanatory experiments.
