A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence

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dc.contributor.authorAtay, Feyza Firat
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.contributor.authorElkiran, Emin Tamer
dc.contributor.authorMansuri, Nasrin
dc.contributor.authorAhmad, Fuzail
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2026-04-04T13:31:18Z
dc.date.available2026-04-04T13:31:18Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground 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.Methods 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.Results 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.Conclusion 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.
dc.description.sponsorshipDeanship of Research and Graduate Studies at King Khalid University [RGP-2/205/45]; Almaarefa University
dc.description.sponsorshipThe author(s) declare financial support was received for the research, authorship, and/or publication of the article. The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through a Large Research Project under grant number RGP-2/205/45. Dr. Fuzail Ahmad would like to thanks Almaarefa University for the support of this study.
dc.identifier.doi10.3389/fmed.2024.1461372
dc.identifier.issn2296-858X
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0002-1189-2206
dc.identifier.pmid39430590
dc.identifier.scopus2-s2.0-85207002236
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fmed.2024.1461372
dc.identifier.urihttps://hdl.handle.net/11616/108706
dc.identifier.volume11
dc.identifier.wosWOS:001334200300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectdifferentiated thyroid cancer
dc.subjectrecurrence prediction
dc.subjectassociative classification
dc.subjectmachine learning
dc.subjectpersonalized medicine
dc.titleA hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence
dc.typeArticle

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