A Comparative Study of Bayesian-Optimized Machine Learning Models for Differentiated Thyroid Cancer Recurrence Prediction

Section: Research Paper
Issue
Vol. 23 No. 1 (2026): Volume 23 Issue 1
Published
May 31, 2026
Pages
117-130

Abstract

The most prevalent type of thyroid malignancy is the differentiated thyroid cancer (DTC), and predicting its recurrence remains a clinical challenge. This study addresses the growing need for reliable predictive tools by applying machine learning techniques enhanced with Bayesian optimization.  Early detection of the risk of recurrence can significantly improve health maintenance and outcomes. The study develops a comparative framework using four supervised classifiers Logistic Regression, (XGBoost) extreme gradient boost, CatBoost, and (LightGBM) light gradient boosting machine on a clinical dataset related to differentiated thyroid cancer patients. Each Model is trained and evaluated both before and after hyperparameter tuning via Bayesian optimization. Model performance is assessed using accuracy, recall, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The optimized (XGBoost) model achieved the top performance, along, recall, precision, and accuracy of 0.97, 0.99, 0.9870 respectively and an AUC of 0.9737, clearly outperforming its default counterpart. In contrast, CatBoost shows a slight performance drop after optimization, while Logistic Regression and LightGBM exhibit no significant changes. The results demonstrate that Bayesian optimization can substantially enhance model performance depending on the algorithm. This study highlights the effectiveness of optimization techniques in boosting the predictive power of machine learning models in the medical field, particularly in recurrence prediction for differentiated thyroid cancer. 

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Authors

Hevi J Hameed
Didar A Rashid

Identifiers

https://doi.org/10.33899/iqjoss.v23i1.62130
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How to Cite

Hameed, H. . J. ., & Rashid, D. A. . (2026). A Comparative Study of Bayesian-Optimized Machine Learning Models for Differentiated Thyroid Cancer Recurrence Prediction . IRAQI JOURNAL OF STATISTICAL SCIENCES, 23(1), 117–130. https://doi.org/10.33899/iqjoss.v23i1.62130