Explainable AI to predict a complex multifactorial outcome, childhood obesity: Application to clinical epidemiology

Childhood obesity, driven by genetic and epidemiological factors, poses significant health risks, yet traditional machine learning models lack interpretability for clinical use. This study aims to apply Kolmogorov-Arnold Networks (KAN), an explainable machine learning model, to predict body mass index (BMI) at age 8 as an indicator of obesity risk and to develop a publicly accessible prediction tool. We utilized the Raine Study Gen2 cohort (n=2,868) to train KAN and traditional models (such as Random Forest, Gradient Boosting, Lasso, and Multi-Layer Perceptron) using perinatal, early-life, and polygenic risk score (PGS) data collected before age 5. Feature importance was analyzed across all the models. A publicly accessible online calculator was developed for practical use. KAN achieved an R2 of 0.81, outperforming traditional models. Key predictors included Year 5 BMI z-score, mid-arm circumference, occupation of mother, and PGS. The online calculator supports predictions without PGS, maintaining an R2 of 0.81. KAN’s transparent formulas enhance interpretability, offering a practical approach to predicting childhood obesity. The freely accessible tool enables clinicians to implement personalized prevention strategies, advancing precision medicine. KAN model predicts childhood obesity (BMI at age 8), showcasing key features, top performance, and accurate formularised results with epidemiological and genetic factors. Online calculator is available at https://bmi-y8-calc.onrender.com/.