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

نویسندگان

چکیده

Continuous advancement in the health sector is essential to reduce costs and increase efficiency quality of service. The widespread use ultrasonography (USG) has made it possible detect thyroid nodules with higher success rates. Some standard scoring systems have been developed score nodules. Thyroid are classification that determine risk cancer according ultrasonographic characteristics nodule size. Different results for same may occur these different systems, which can cause some unnecessary surgical interventions. In this study, intelligent models assist aim correct intervention operation by preventing interventions procedures. integration current (K-TIRADS, ACR-TIRADS, EU-TIRADS, ATA, BTA) machine learning methods provides radiologists clinicians a decision-support mechanism evaluation Correct diagnosis will help helping prevent present dataset was retrospectively constructed using ultrasound images between 2014 2018. determining treatment process nodules, Random Forest, Adaboost, J48 Decision Tree (J48 DT), Support Vector Machine (SVM) used increased prediction accuracy systems. goal decrease redundant Fine Needle Aspiration (FNA) biopsies suspicious As result degrees achieved determination or incorrect DT algorithm EU-TIRADS system, an rate 99.7853%, compared other classifiers.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2022

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app122211559