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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Cengel, Talha Alperen
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Differentiated Thyroid Cancer Recurrence Prediction Using Boosting Algorithms Saritas, Mucahid Mustafa; Yildiz, Muslume Beyza; Cengel, Talha Alperen; Koklu, Murat
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.490

Abstract

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.
Co-Authors Koklu, Murat Saritas, Mucahid Mustafa Yildiz, Muslume Beyza