The Efficiency of Ensemble Techniques in Predicting Thyroid Disorder: A Comparative Study

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dc.contributor.authorAlsaadawi, M.
dc.contributor.authorSehirli, E.
dc.date.accessioned2024-09-29T16:20:47Z
dc.date.available2024-09-29T16:20:47Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- Ankara -- 184355en_US
dc.description.abstractData science is presently connected with a wide range of technical and scientific fields. Thyroid disorder is a widespread issue that affects a great variety of people. Hospitals report several forms of thyroid conditions. In this thesis, a thyroid disease prediction model has been created by classification and comparing traditional and Ensemble algorithms. A dataset including 1,250 records from the Iraqi people was utilized for the first-time using Ensemble methods. Stacking is one of the most effective Ensemble approaches for forecasting complicated structured data. Several metrics, including Accuracy, Precision, Sensitivity, Specificity, F-Score, and the Matthews correlation coefficient, were used to evaluate the performance of the prediction model. The experimental findings show that the proposed technique to optimize the detection of thyroid illnesses may be successfully implemented. The majority of Ensemble methods achieved 100 % accuracy with both the whole data set and the feature selection data set. In terms of precision and computational expense, the given findings outperform comparable models in their field. © 2022 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT56059.2022.9932774
dc.identifier.endpage840en_US
dc.identifier.isbn978-166547013-1
dc.identifier.scopus2-s2.0-85142785200en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage834en_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT56059.2022.9932774
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9327
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectaccuracyen_US
dc.subjectensembleen_US
dc.subjectfeature selectionen_US
dc.subjectmachine learningen_US
dc.subjectRFEen_US
dc.subjectSMOTEen_US
dc.subjectstackingen_US
dc.subjectthyroid diseaseen_US
dc.titleThe Efficiency of Ensemble Techniques in Predicting Thyroid Disorder: A Comparative Studyen_US
dc.typeConference Objecten_US

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