<![CDATA[Thyroid disorders are common endocrine conditions whose diagnosis often requires integrating multiple clinical and laboratory indicators. This study proposes a machine learning framework for multiclass classification of thyroid diseases using XGBoost combined with an automated preprocessing and feature-engineering pipeline. A dataset of 9,167 patient records and 30 clinical and biochemical features was processed using a structured pipeline that included imputation, encoding, scaling, and hyperparameter optimization with RandomizedSearchCV and GridSearchCV. The optimized XGBoost model achieved 95.20% test accuracy, a high weighted F1-score (0.94), and consistent cross-validated performance. Classification results showed excellent discrimination for major thyroid conditions and reliable identification of healthy individuals. Feature importance analysis revealed that TBG-related measurements, thyroxine therapy status, and key hormone indices (TSH, TT4, FTI) were the most influential predictors. Overall, the findings demonstrate that the proposed XGBoost-based framework provides accurate and robust support for multiclass thyroid disease diagnosis and can serve as a practical foundation for clinical decision-support applications.]]>
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