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ORIGINAL RESEARCH article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1461372
This article is part of the Research Topic Progressive Role of Artificial Intelligence in Treatment Decision - Making in the Field of Medical Oncology View all 4 articles

A Hybrid Machine Learning Model Combining Association Rule Mining and Classification Algorithms to Predict Differentiated Thyroid Cancer Recurrence

Provisionally accepted
Feyza Firat Atay Feyza Firat Atay 1Fatma Hilal Yagin Fatma Hilal Yagin 2*Cemil Çolak Cemil Çolak 2Emin Tamer Elkiran Emin Tamer Elkiran 1Nasrin Mansuri Nasrin Mansuri 3Fuzail Ahmad Fuzail Ahmad 4Luca P. Ardigò Luca P. Ardigò 5*
  • 1 Faculty of Medicine, Inönü University, Malatya, Türkiye
  • 2 Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye
  • 3 Department of Clinical Laboratory Sciences, College of Applied Medical Science, King Khalid University, Abha, Saudi Arabia
  • 4 Department of Respiratory Care, College of Applied Sciences, University of Almaarefa, Dariyah, Riyadh, Saudi Arabia
  • 5 Department for Teacher Education, NLA University College, Oslo, Norway

The final, formatted version of the article will be published soon.

    Background: Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions.This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.

    Keywords: Differentiated thyroid cancer, recurrence prediction, associative classification, machine learning, personalized medicine

    Received: 08 Jul 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Firat Atay, Yagin, Çolak, Elkiran, Mansuri, Ahmad and Ardigò. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Fatma Hilal Yagin, Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye
    Luca P. Ardigò, Department for Teacher Education, NLA University College, Oslo, 0130, Norway

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.