Reducing Operation Costs of Thyroid Nodules Using Machine Learning Algorithms with Thyroid Nodules Scoring Systems

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dc.authoridTurkoglu, Huseyin/0000-0003-1249-0580
dc.authoridAYVAZ, Ednan/0000-0002-5328-1884
dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.authoridKUNCAN, Fatma/0000-0003-0712-6426
dc.authoridAyvaz, Erdal/0000-0002-4630-278X
dc.contributor.authorAyvaz, Erdal
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorKuncan, Fatma
dc.contributor.authorAyvaz, Ednan
dc.contributor.authorTurkoglu, Huseyin
dc.date.accessioned2024-12-24T19:33:32Z
dc.date.available2024-12-24T19:33:32Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractContinuous advancement in the health sector is essential to reduce costs and increase efficiency and quality of service. The widespread use of ultrasonography (USG) has made it possible to detect thyroid nodules with higher success rates. Some standard scoring systems have been developed to score thyroid nodules. Thyroid scoring systems are classification systems that determine the risk of cancer in thyroid nodules according to ultrasonographic characteristics and nodule size. Different scoring results for the same thyroid nodule may occur according to these different scoring systems, which can cause some unnecessary surgical interventions. In this study, some intelligent models are developed to assist thyroid scoring systems, with the aim to determine the correct surgical intervention and reduce operation costs by preventing unnecessary interventions and surgical procedures. The integration of current thyroid scoring systems (K-TIRADS, ACR-TIRADS, EU-TIRADS, ATA, and BTA) and machine learning methods provides radiologists and clinicians a decision-support mechanism in the evaluation of thyroid nodules. Correct diagnosis will help to reduce costs by helping prevent unnecessary procedures. The present dataset was retrospectively constructed using ultrasound images of thyroid nodules between 2014 and 2018. In determining the treatment process of thyroid nodules, Random Forest, Adaboost, J48 Decision Tree (J48 DT), and Support Vector Machine (SVM) models are used for increased prediction accuracy of thyroid scoring systems. The goal is to decrease redundant Fine Needle Aspiration (FNA) biopsies and surgical interventions of suspicious thyroid nodules. As a result of the study, higher degrees of accuracy are achieved in the determination of correct or incorrect surgical interventions of thyroid nodules using the J48 DT algorithm with the EU-TIRADS scoring system, with an accuracy rate of 99.7853%, compared to other classifiers.
dc.identifier.doi10.3390/app122211559
dc.identifier.issn2076-3417
dc.identifier.issue22
dc.identifier.scopus2-s2.0-85142488321
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app122211559
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8182
dc.identifier.volume12
dc.identifier.wosWOS:000887133000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectthyroid nodules
dc.subjectclassification systems
dc.subjectmachine learning
dc.subjectdiagnosis
dc.subjectlean costing
dc.titleReducing Operation Costs of Thyroid Nodules Using Machine Learning Algorithms with Thyroid Nodules Scoring Systems
dc.typeArticle

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