The Prediction of Thyroid Cancer Recurrence with the XGBoost Method: The Clinicopathological Feature-Based Approach
DOI:
10.47709/cnahpc.v6i3.4101Keywords:
Thyroid Cancer, Xgboost, Clinicopathological FeatureDimension Badge Record
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.
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References
Andriansyah, D.-, & Fridayanthie, E. W. (2023). Optimization of Support Vector Machine and XGBoost Methods Using Feature Selection to Improve Classification Performance. JITE (Journal of Informatics and Telecommunication Engineering), 6(2), 484–493. https://doi.org/10.31289/JITE.V6I2.8373
Bogar, B. D., Lumintang, N., Tandililing, S., & Wariki, W. M. (2024). Korelasi antara Kadar Kalsidiol (25-Oh) dan Rasio Netrofil-Limfosit dengan Keganasan Nodul Tiroid. E-CliniC, 12(2), 205–212. https://doi.org/10.35790/ECL.V12I2.48492
Andinata, B. (n.d.). Kanker Tiroid, Kenali Gejala, Penyebab, dan Cara Pengobatannya! Retrieved June 14, 2024, from https://www.siloamhospitals.com/informasi-siloam/artikel/kanker-tiroid
Dwi Arista, R., Karima, K., Anugrah, M. F., Widyastuti, P., & Triani, E. (2023). Thyroid Cancer?: an Overview of Epidemiology, Risk Factor, and Treatment. Lombok Medical Journal, 2(2), 90–96. https://doi.org/10.29303/LMJ.V2I2.2791
Fachri, P. (2024). Gambaran Klinikopatologi Kanker Tiroid di RSUP Dr. M. Djamil Padang Tahun 2018-2020. http://scholar.unand.ac.id/466745/
Handayani, S. H. S., Handayani, S. H. S., & Purnami, S. W. (2014). Pendekatan Metode Classification and Regression Tree untuk Diagnosis Tingkat Keganasan Kanker pada Pasien Kanker Tiroid. Jurnal Sains Dan Seni ITS, 3(1), D24–D29. https://doi.org/10.12962/j23373520.v3i1.6108
Kurnia, D., Mazdadi, M. I., Kartini, D., Nugroho, R. A., Abadi, F., & Korespondensi, P. (2023). Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost. Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(5), 1083–1094. https://doi.org/10.25126/JTIIK.20231057252
Nababan, A. A., Jannah, M., Aulina, M., & Andrian, D. (2023). Prediksi Kualitas Udara Menggunakan Xgboost Dengan Synthetic Minority Oversampling Technique (SMOTE) Berdasarkan Indeks Standar Pencemaran Udara (ISPU). Jurnal Teknik Informatika Kaputama (JTIK), 7(1).
Nugraha, W. (2021). Prediksi Penyakit Jantung Cardiovascular Menggunakan Model Algoritma Klasifikasi. JURNAL SIGMATA, 9(2), 78–84. https://www.kaggle.com/andrewmvd/heart-
Nur, A., Santosa, A., & Siti Komariyah, A. (2023). Karakteristik Kanker Tiroid Di Maluku Utara Tahun 2017-2020. Jurnal Endurance, 8(2), 246–252. https://doi.org/10.22216/JEN.V8I2.2161
Parura, Y., Pontoh, V., & Merung, M. (2016). Pola kanker tiroid periode Juli 2013 - Juni 2016 di RSUP Prof. Dr. R. D Kandou Manado. Jurnal E-Clinic (ECl), 4(2). https://doi.org/10.35790/ECL.V4I2.14475
Ravly Andryan, M., Fajri, M., & Nina Sulistyowati, dan. (2022). Komparasi Kinerja Algoritma Xgboost Dan Algoritma Support Vector Machine (SVM) Untuk Diagnosis Penyakit Kanker Payudara. JIKO (Jurnal Informatika Dan Komputer), 6(1), 1–5. https://doi.org/10.26798/JIKO.V6I1.500
Salsabil, M., Azizah, N. L., & Eviyanti, A. (2024). Implementasi Data Mining Dalam Melakukan Prediksi Penyakit Diabetes Menggunakan Metode Random Forest Dan Xgboost. Jurnal Ilmiah Komputasi, 23(1), 51–58. https://doi.org/10.32409/JIKSTIK.23.1.3507
Siswandi, A., Fitriyani, N., Artini, I., & Monitira, K. (2020). Karakteristik Penderita Kanker Tiroid Di Bagian Bedah Onkologi Di Rumah Sakit Umum Daerah Dr. H. Abdul Moeloek Provinsi Lampung Tahun 2017-2019. Jurnal Medika Malahayati, 4(3), 244–248.
Yulianti, S. E. H., Soesanto, O., & Sukmawaty, Y. (2022). Penerapan Metode Extreme Gradient Boosting (XGBOOST) pada Klasifikasi Nasabah Kartu Kredit. Journal of Mathematics: Theory and Applications, 4(1), 21–26. https://doi.org/10.31605/JOMTA.V4I1.1792
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