Interpretable RNN for Prediction & Understanding of Childhood Obesity: A Scenario from the UK Millennium Cohort Study

Childhood obesity is a growing public health concern worldwide, with significant implications for long-term health outcomes and healthcare costs. Early identification of children at risk of obesity is essential for implementing effective prevention and intervention strategies. In recent years, machine learning techniques have emerged as powerful tools for predicting childhood obesity risk based on various predictors such as demographic factors, dietary habits, physical activity levels, and genetic predisposition. This paper explores the use of temporal deep-learning models, specifically recurrent neural networks and long short-term memory, to predict obesity childhood stages using longitudinal datasets. The long short-term memory model emerges as the top performer, achieving an accuracy of 85% and an F1-score of 0.85, surpassing existing models. To enhance model transparency, model-agnostic methods are employed, revealing critical insights into model predictions. Results indicate that BMI at age 11 is the most influential feature in predicting obesity. These findings underscore the importance of applicability models and explainable machine learning methods in understanding and predicting childhood obesity progression.