###### Abstract  
Continuous 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. thyroid nodules; classification systems; machine learning; diagnosis; lean costing Article  
Reducing Operation Costs of Thyroid Nodules Using Machine Learning Algorithms with Thyroid Nodules Scoring Systems  
[PERSON], [PERSON]; [PERSON], [PERSON], H.  
to the patient because of the treatments performed by health personnel with insufficient knowledge, skills, experience, or interest [6]. However, not only does medical malpractice affect patient experience and efficiency in healthcare, but it also includes important negative factors such as delays, long waiting times, waiting lists for service delivery, lack or excess of capacity, lack or excess of inventory, patients who go to the hospital multiple times before service is delivered, and dissatisfied patients. A healthcare organization should value its patients and their relatives. Therefore, patient value should be the key to making responsible decisions. Patient value can be defined as the patient's medical outcome divided by the cumulative cost [5]. Thyroid nodules are separate lesions located within the thyroid gland that have radiologically different features from the surrounding thyroid parenchyma [7]. They are relatively prevalent in the general population, with an incidence of 10-67% [8; 9; 10]. Although most thyroid nodules are benign, 9-15% are determined to be malignant according to the results of ultrasound-guided fine-needle aspiration (FNA) [7]. Therefore, evaluating the specific US findings that can distinguish benign thyroid nodules from malignant ones helps to avoid unnecessary FNA or frequent follow-up US. Also, early detection of benign and malignant thyroid nodules is considered vital to guide clinical treatment or, if necessary, to decide on operative methods. After evaluating a thyroid nodule, agreeing on the most appropriate strategy to manage malignant nodules may help avoid redundant procedures and surgery for benign nodules. In this sense, conventional ultrasound (US) is commonly preferred to evaluate thyroid nodules, since it is noninvasive and cost-effective [11].