Table 1_Development and validation of an interpretable machine learning model for predicting ADHD comorbidity in children with allergic rhinitis: a SHAP-based analysis.docx

Background<p>The co-occurrence of allergic rhinitis (AR) and attention-deficit/hyperactivity disorder (ADHD) presents significant clinical challenges in pediatrics, yet predictive tools capturing non-linear neuro-immune interactions are lacking.</p>Methods<p>We developed an interpretable Extreme Gradient Boosting (XGBoost) model utilizing a hybrid feature selection pipeline on a cohort of 518 pediatric AR patients (255 with comorbid ADHD).

Model performance was evaluated via an independent test set. Global and local interpretability was decoded using SHapley Additive exPlanations (SHAP).</p>Results<p>A parsimonious subset of six predictors was identified. The XGBoost model achieved superior discriminative capacity (AUC = 0.878) compared to conventional linear baselines.