Citation

Shrestha, Keshika and Rifat, H. M. Jabed Omur and Biswas, Uzzal and Tiang, Jun Jiat and Nahid, Abdullah-Al (2025) Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm. Diagnostics, 15 (13). p. 1684. ISSN 2075-4418

Text Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Differentiated Thyroid Cancer (DTC), comprising papillary and follicular carcinomas, is the most common type of thyroid cancer. This is highly infectious and increasing at a higher rate. Some patients experience recurrence even after undergoing successful treatment. Early signs of recurrence can be hard to identify, and the existing health care system cannot always identify it on time. Therefore, predicting its recurrence accurately and in its early stage is a significant clinical challenge. Numerous advanced technologies, such as machine learning, are being used to overcome this clinical challenge. Thus, this study presents a novel approach for predicting the recurrence of DTC. The key objective is to improve the prediction accuracy through hyperparameter optimization.

Item Type:	Article
Uncontrolled Keywords:	Thyroid cancer
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Jul 2025 05:10
Last Modified:	01 Aug 2025 03:16
URII:	http://shdl.mmu.edu.my/id/eprint/14379

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