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

Background: 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. Methods: 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). Results: A parsimonious subset of six predictors was identified. The XGBoost model achieved superior discriminative capacity (AUC = 0.878) compared to conventional linear baselines. SHAP topographical analysis revealed profound non-linear thresholds and uncovered a critical synergistic interaction: elevated ADHD risk was strongly correlated with patients presenting simultaneously severe allergic inflammation (Total IgE > 250 IU/mL) and nutritional deficiency (Vitamin D < 20 ng/mL). Furthermore, Decision Curve Analysis (DCA) demonstrated substantial net clinical benefit for triage application. Conclusion: We established a highly robust, interpretable machine learning algorithm capable of accurately stratifying ADHD risk among AR children. The identified neuro-immune synergistic phenotypes provide critical mechanistic insights and offer a practical decision-support tool for timely pediatric multidisciplinary interventions.