Explainable AI-Based Thyroid Diagnosis Classification Using SHAP-Guided Feature Reduction

In this study, we developed an explainable machine learning framework for benign–malignant thyroid diagnosis classification using structured tabular data. We evaluated seven classifiers through a consistent preprocessing and class-imbalance pipeline.
CatBoost achieved 82.93% test accuracy, 69.92% balanced accuracy, and 55.41% F1-score. Our three-fold cross-validation produced 82.69% mean accuracy and confirmed the close performance of the leading boosting models.We used SHAP to identify eight influential predictors, and the corresponding Top-8 representation preserved the principal classification metrics of the original 14-feature model. Our central contribution is therefore a compact and interpretable feature representation rather than a new learning algorithm.