Personalized Thyroid Disorder Diagnosis System through Adaptive Expert Routing and Intelligent Selection

The main contribution of this work is AERIS, an adaptive expert-routing framework that performs patient-specific selection among heterogeneous machine learning models for thyroid disorder diagnosis. By combining a meta-learning router with Random Forest, LightGBM, CatBoost, and XGBoost, the framework achieves 97.87% accuracy and 95.98% F1-score, while SHAP-based analysis provides interpretable insights into the clinical features influencing predictions.