2026-10-14T21:17:21Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33970987 2026-09-23T05:44:36Z category_427 portal_316 item_type_3 month_year_09_2026

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 Hexiu Yuan (22010696) Nengjing Wu (25094416) Public Health and Health Services not elsewhere classified allergic rhinitis attention-deficit/hyperactivity disorder machine learning neuro-immune crosstalk SHAP 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. 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.</p>Conclusion<p>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.</p> 2026-09-23T05:44:36Z Dataset Dataset 10.3389/fpubh.2026.1837156.s001 https://figshare.com/articles/dataset/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/33970987 CC BY 4.0