The Prediction of Thyroid Cancer Recurrence with the XGBoost Method: The Clinicopathological Feature-Based Approach

Abstract

This research aims to develop a thyroid cancer recurrence prediction model using the XGBoost method with a clinicopathological feature-based approach. Thyroid cancer is one of the cancers that have a significant recurrence rate after initial treatment. Therefore, thyroid cancer recurrence prediction is important in determining treatment plans and patient management. In this study, we used a dataset containing 383 records of clinicopathological information on thyroid cancer patients who had undergone treatment. The features include various clinical and pathological parameters that are considered important in recurrence prediction. We used the XGBoost algorithm, which has proven effective in various classification tasks, to build a prediction model. The model evaluation results show good consistency in predicting the thyroid cancer recurrence with an average accuracy value of around 97.74% and an average F1-score value of around 95.94%. The results show that the XGBoost model can provide thyroid cancer recurrence prediction with good accuracy, with the ability to effectively detect both classes (recurrence and non-recurrence). The model is expected to be a valuable tool in supporting clinical decision-making related to the management of thyroid cancer patients.