Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data

سال انتشار: 1401
محل انتشار: مجله تحقیقات در علوم سلامت، دوره: 22، شماره: 3
کد COI اختصاصی: JR_JRHSU-22-3_001
زبان مقاله: انگلیسی
تعداد مشاهده: 201

نویسندگان

MSc, Student Research Committee, School of Medicine, Faculty of Health, Babol University of Medical Science, Babol, Iran

Naser Chaemian

PhD, Department of Radiology, Babol University of Medical Sciences, Babol, Iran

Soraya Khafri

PhD, Research Center for Social Determinants of Health, Health Research Institute, Department of Biostatistics and Epidemiology, Faculty of Health, Babol University of Medical Sciences, Babol, Iran

Mehdi Ezoji

PhD, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

چکیده

Background: This study aims to show the impact of imbalanced data and the typical evaluation methods in developing and misleading assessments of machine learning-based models for preoperative thyroid nodules screening. Study design: A retrospective study. Methods: The ultrasonography features for ۴۳۱ thyroid nodules cases were extracted from medical records of ۳۱۳ patients in Babol, Iran. Since thyroid nodules are commonly benign, the relevant data are usually unbalanced in classes. It can lead to the bias of learning models toward the majority class. To solve it, a hybrid resampling method called the Smote-was used to creating balance data. Following that, the support vector classification (SVC) algorithm was trained by balance and unbalanced datasets as Models ۲ and ۳, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model ۱ that fitted traditionally. Results: The prevalence of malignant nodules was obtained at ۱۴% (n = ۶۱). In addition, ۸۷% of the patients in this study were women. However, there was no difference in the prevalence of malignancy for gender. Furthermore, the accuracy, area under the curve, and geometric mean values were estimated at ۹۲.۱%, ۹۳.۲%, and ۷۶.۸% for Model ۱, ۹۱.۳%, ۹۳%, and ۷۷.۶% for Model ۲, and finally, ۹۱%, ۹۲.۶% and ۸۴.۲% for Model ۳, respectively. Similarly, the results identified Micro calcification, Taller than wide shape, as well as lack of ISO and hyperechogenicity features as the most effective malignant variables. Conclusion: Paying attention to data challenges, such as data imbalances, and using proper criteria measures can improve the performance of machine learning models for preoperative thyroid nodules screening.

کلیدواژه ها

Machine learning, Support vector machines, Thyroid nodule, Ultrasonography

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.