<![CDATA[Background of study: Differentiated thyroid cancer (DTC) accounts for most thyroid malignancies and has favorable survival outcomes, yet up to 30% of patients experience recurrence, placing strain on follow-up systems in resource-limited settings. Conventional staging tools offer limited predictive precision. With increasing interest in machine learning (ML) for precision oncology, there is a need for interpretable, deployable models suitable for low-resource environments.Aims and scope of paper: To develop and validate an interpretable machine learning model for predicting thyroid cancer recurrence and assess its feasibility for deployment in constrained clinical settings, including African oncology contexts.Methods: A retrospective dataset of 383 DTC patients with at least 10-year follow-up was sourced from the UCI Machine Learning Repository. Thirteen demographic, clinical, and treatment-related predictors were included. Data preprocessing involved encoding, scaling, and class balancing using SMOTE. Logistic Regression, Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost) were trained with hyperparameter tuning via grid search and cross-validation. Performance was evaluated using accuracy, precision, recall, F1 score, and AUC-ROC.Result: XGBoost achieved the best performance with 97% accuracy, 95% recall, 94% precision, and an AUC-ROC of 0.93. The most influential predictors were age, smoking status, T and M staging, ATA risk category, and adenopathy. The final model was deployed as a browser-based decision support tool to enable real-time recurrence risk estimation.Conclusion: This study presents a high-performing and interpretable ML model for predicting DTC recurrence, demonstrating feasibility for use in low-resource oncology settings. External validation with African clinical datasets and integration into electronic health systems is recommended to enhance equity and clinical uptake.]]>
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