Machine Learning Approaches to Predict Thyroid Cancer Recurrence: A Comparative Study

Thyroid cancer recurrences have significant impacts on patients' health. The current clinical methods have limited prediction accuracy because of inadequate incorporation of individual patient variability. Therefore, there is a need for advanced techniques that can help medical experts to make accurate and reliable predictions. Recent efforts on the topic are focused on machine learning algorithms. This study aims to assess the performance of these algorithms for predicting thyroid cancer recurrence. Four prediction models are trained using Gradient Boosting, Random Forest, XGBoost, and AdaBoost algorithms. The performances of the models are evaluated with metrics such as accuracy, F1 score, F-05 score, and area under the ROC curve (AUC). Experiments with the K-Fold (5-fold) method have revealed how effective these models can be in the prediction of thyroid cancer recurrence. The performance of the machine learning models was evalu-ated using accuracy, Fl-score, F05-score, and AUC score metrics. The XGBoost model outperformed others, achieving an accuracy of 95,3%, Fl-score of 91,8%, F05-score of 91,9%, and AUC score of 98,2%. These results show that machine learning-based solutions may contribute to incorporating more accurate and individualized treatment decisions in clinical practice.