Explainable Ensemble Machine Learning Framework for Accurate Detection of Thyroid Disorders

• We have introduced a highly developed Ensemble-Based Thyroid Disorder Detector (ETDD) that uses Random Forest, CatBoost, LightGBM, and XGBoost in sequence, and feature engineering has been used to enhance the detector’s effectiveness.

• We have improved model interpretability and clinician trust by integrating explainable AI techniques, including SHAP and LIME, to explain predictions and validate model decisions.

• We have conducted a comprehensive evaluation against state-of-the-art models, demonstrating that the ETDD system achieves a high accuracy of 96.24%, establishing its effectiveness as a clinical decision-support tool.