Predicting Thyroid Cancer Recurrence After Radioactive Iodine Therapy Using Random Forest and Neural Network Models

Thyroid cancer recurrence following Radioactive Iodine (RAI) therapy remains a clinical concern, necessitating accurate and timely risk prediction to guide post-treatment management. This study aims to evaluate the effectiveness of machine learning models—Random Forest and Neural Networks—in predicting recurrence using a structured clinical dataset consisting of 383 patient records and 13 diagnostic and pathological attributes. All categorical features were encoded ordinally, and the dataset was partitioned into training and testing sets with appropriate normalization for neural network processing. Both models were evaluated using standard metrics including accuracy, precision, recall, and F1-score. The Random Forest model achieved an accuracy of 97.39%, outperforming the Neural Network which recorded 93.04%. Moreover, Random Forest showed better recall in detecting recurrence cases, making it a more suitable model for clinical application. These results demonstrate that machine learning, particularly ensemble-based methods, can offer a practical and interpretable solution for recurrence prediction, supporting data-driven decision-making in thyroid cancer follow-up care.