ThyroTrans-XGB: A Hybrid Transformer and Gradient Boosting Framework for Thyroid Disease Classification

Thyroid disease classification is an important healthcare task because thyroid dysfunction affects metabolism, hormonal balance, cardiovascular activity, and overall well-being. Accurate detection can support timely treatment; however, prediction remains challenging because of nonlinear relationships among biochemical markers, clinical-history variables, missing values, and imbalanced classes. This study proposes ThyroTrans-XGB, a hybrid framework combining Transformer-based feature representation with XGBoost >= 1.0.0 classification. Two publicly available thyroid disease datasets were examined in separate benchmark experiments. The Kaggle Thyroid Disease Dataset was used for binary classification, whereas the UCI Thyroid Disease Dataset was used for a separate multiclass benchmark evaluation. The model was separately trained and evaluated within each dataset-specific setting. The Transformer module learns attention-based representations of thyroid-related variables, while XGBoost performs final classification. Performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, Matthews correlation coefficient, calibration analysis, statistical testing, and SHAP-based explainable artificial intelligence (XAI). ThyroTrans-XGB achieved an F1-score of 0.9828, ROC-AUC of 0.9997, and PR-AUC of 0.9948 on the Kaggle benchmark, and a macro-F1-score of 0.9891, ROC-AUC of 0.9999, and PR-AUC of 0.9928 on the UCI benchmark. Since both datasets were separately trained and evaluated and may share public-data provenance, the UCI experiment represents benchmark evaluation rather than external validation.