Using Machine Learning Methods in Early Diagnosis of Breast Cancer
| dc.contributor.author | Erkal, Begum | |
| dc.contributor.author | Ayyildiz, Tulin Ercelebi | |
| dc.contributor.orcID | https://orcid.org/0000-0002-7372-0223 | en_US |
| dc.contributor.researcherID | JBI-6492-2023 | en_US |
| dc.date.accessioned | 2023-09-11T11:47:50Z | |
| dc.date.available | 2023-09-11T11:47:50Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Breast cancer is one of the most important health diseases to be treated in the world, and it is a subject that has a wide place in research subjects. In this study, results are provided by using seven different machine learning techniques for the classification of breast cancer. In order to obtain better results, the preprocessing method was applied. As a result, when compared with some studies in the literature, it was observed that the general performance of some of the methods improved. In the experimental results, BayesNet was found to be the best classification method with an accuracy rate of 97.13%. | en_US |
| dc.identifier.isbn | 978-1-6654-3663-2 | en_US |
| dc.identifier.scopus | 2-s2.0-85123678683 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11727/10569 | |
| dc.identifier.wos | 000903766500043 | en_US |
| dc.language.iso | eng | en_US |
| dc.relation.isversionof | 10.1109/TIPTEKNO53239.2021.9632975 | en_US |
| dc.relation.journal | TIP TEKNOLOJILERI KONGRESI (TIPTEKNO'21) | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Classification | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | breast cancer | en_US |
| dc.subject | rbayesnet | en_US |
| dc.title | Using Machine Learning Methods in Early Diagnosis of Breast Cancer | en_US |
| dc.type | conferenceObject | en_US |
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