Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing
Alawiyah, Tuti Alawiyah
Unknown Affiliation
Computer Science & IT Education

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
The Prediction of Thyroid Cancer Recurrence with the XGBoost Method: The Clinicopathological Feature-Based Approach Alawiyah, Tuti Alawiyah; Wibisono, Taufik; Yani Sri Mulyani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4101

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.
Co-Authors Wibisono, Taufik yani sri mulyani