Background: This study aimed to predict the recurrence of differentiated thyroid cancer and identify its most representative risk factors using an explainable artificial intelligence model. Methods: The publicly available Differentiated Thyroid Cancer Recurrence dataset from the University of California Irvine Machine Learning Repository, comprising 383 patients and 17 features, was employed. Five classifiers, -Random Forest, Gradient Boosting, AdaBoost, Support Vector Classifier and Logistic Regression-, were employed to predict the recurrence. Permutation feature importance (PFI) and SHapley Additive exPlanations (SHAP) explainable artificial intelligence methods were used to determine the features that had the most impact on the prediction result. Results: The Random Forest algorithm outperformed others, achieving an accuracy of 97.39% and an Area under the Curve of 0.993. Response to treatment, ATA risk stratification, tumor stage and patient age were determined as the factors with the highest contribution to the model prediction process through SHAP and permutation importance analyses, and this finding was consistent with the prognostic markers stated in the literature. Conclusion: The proposed explainable machine learning framework has shown satisfactory results in predicting DTC recurrence while identifying clinically important features. This approach can offer valuable support to clinicians in early identification of high-risk patients and personalization of surveillance strategies.
Öztürk et al. (Sun,) studied this question.