Differentiated Thyroid Cancer Recurrence Prediction Using Boosting Algorithms - Mucahid Mustafa Saritas; Muslume Beyza Yildiz; Talha Alperen Cengel; Murat Koklu (LKP KARYA PRIMA KURSUS, 2025)
This study aims to compare the performance of AdaBoost, Gradient Boosting, and CatBoost algorithms in predicting the recurrence risk of Differentiated Thyroid Cancer (DTC). DTC is the most common type of thyroid cancer, and due to its recurrence risk, accurate and effective prediction models are needed. In this study, a dataset containing clinical and pathological data of patients diagnosed with DTC was used. The performance of the models was evaluated using metrics such as accuracy, precision, recall, and F1 score. The results revealed that the CatBoost algorithm achieved the highest performance, with an accuracy of 98.70% and an F1 score of 98.69% on the test data. The Gradient Boosting algorithm ranked second with an accuracy of 97.40% and an F1 score of 97.40%, while the AdaBoost algorithm showed the lowest performance, with an accuracy of 96.10% and an F1 score of 96.14%. These findings indicate that the CatBoost algorithm outperforms the other algorithms in predicting DTC recurrence risk and is a suitable candidate for use in clinical decision support systems.