CatBoost with physics-based metaheuristics for thyroid cancer recurrence prediction.

Thyroid Cancer (TC) is the uncontrolled growth of carcinogenic cells in the thyroid gland, with a higher recurrence rate than other cancers. Early detection of TC recurrence (TCR) is crucial for timely intervention. This study develops machine-learning algorithms that reduce features while maintaining high performance. Previous studies on the Differentiated Thyroid Cancer Recurrence (DTCR) dataset struggled to improve performance with feature reduction, and misclassification causes remained unexplored. This work proposes three Physics-based Metaheuristic Algorithms (PBMHAs)—Energy Valley Optimization (EVOA), Equilibrium Optimization (EOA), and Electromagnetic Field Optimization (EFOA)—combined with the Categorical Boosting (CatBoost) classifier. SHAP is used to analyze feature importance. CatBoost without optimization (Only CB) achieved 95.83% Accuracy, 92.42% F-score, 96.29% Precision, and 89.27% Recall using all 16 features. After optimization, EVOA_CB reached 96.35% mean accuracy, while EOA_CB and EFOA_CB achieved 96.17%. EOA_CB excluded 11 less important features, and EFOA_CB attained the highest mean AUC of 0.994 with the lowest computational times. Additionally, this work provides insights into the factors contributing to misclassification. Using a 30:70 train-test split over 5 folds, EVOA_CB performed best on six selected features, with 96.35% Accuracy, 93.34% F-score, and 96.19% Precision. SHAP highlighted response, risk, and N as the most important features. These findings support early, efficient detection of TC recurrence with fewer features.