Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.
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 43...
| Publicado en: | Journal of Research in Health Sciences Vol. 22; no. 3; pp. 1 - 9 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Summer2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160121885&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160121885 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Summer2022 vid: 22 iid: 3 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 160121885 160121885 160121885 10.34172/jrhs.2022.90 160121885 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data. aug: au: Khodabandelu, Sajad Ghaemian, Naser Khafri, Soraya Ezoji, Mehdi Khaleghi, Sara affil: Student Research Committee, School of Medicine, Faculty of Health, Babol University of Medical Science, Babol, Iran sug: subj: Thyroid Nodule Diagnosis Thyroid Nodule Classification Health Screening Methods Machine Learning Preoperative Period Human Male Female Adult Middle Age Aged Odds Ratio Confidence Intervals Descriptive Statistics Data Analysis Software Retrospective Design Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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 431 thyroid nodules cases were extracted from medical records of 313 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 2 and 3, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model 1 that fitted traditionally. Results: The prevalence of malignant nodules was obtained at 14% (n = 61). In addition, 87% 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 92.1%, 93.2%, and 76.8% for Model 1, 91.3%, 93%, and 77.6% for Model 2, and finally, 91%, 92.6% and 84.2% for Model 3, 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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