Article

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

2025-06-23

Abstract excerpt

<h4>Background</h4> Childhood obesity, driven by genetic and epidemiological factors, poses significant health risks, yet traditional machine learning models lack interpretability for clinical use. <h4>Objective</h4> 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...

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Literature Corpus work: fac7208c-ab8a-55c0-adde-b6db47e87c78
DOI: 10.1101/2025.06.21.25330041

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