Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions

Abstract

Obesity, as an independent risk factor for increased morbidity and mortality throughout the lifecycle, is a major health issue in the United States. Pediatric obesity is a strong risk factor for adult obesity, as it tends to be stable and tracks into adulthood. Therefore, prevention of childhood obesity is urgently required for reduction in obesity prevalence and obesity related comorbidities. In this paper, the general pediatric obesity development pattern and the onset time period of early childhood obesity was identified via analysis of approximately 11 million pediatric clinical encounters of 860,510 unique individuals. XGBoost model was developed to predict at age 2 years if individuals would develop obesity in early childhood. The model is generalized to both males and females, and achieved an AUC of 81% (± 0.1%). Obesity associated risk factors were further analyzed via interpretation of the XGBoost model predictions. Besides known predictive factors such as weight, height, race, and ethnicity, new factors such as body temperature and respiratory rate were also identified. As body temperature and respiratory rate are related to human metabolism, novel physiologic mechanisms that cause these associations might be discovered in future research. We decomposed model recall to different age ranges when obesity incidence occurred. The model recall for individuals with obesity incidence between 24–36 months was 97.63%, while recall for obesity incidence between 72–84 months was 48.96%, suggesting obesity is less predictable further in the future. Since obesity is largely affected by evolving factors such as life style, diet, and living environment, it is possible that obesity prevention may be achieved via changes in adjustable factors.