| dc.contributor.author | Gürbüz E. | |
| dc.contributor.author | Kiliç E. | |
| dc.date.accessioned | 2020-06-21T09:36:51Z | |
| dc.date.available | 2020-06-21T09:36:51Z | |
| dc.date.issued | 2011 | |
| dc.identifier.isbn | 9.78146E+12 | |
| dc.identifier.uri | https://doi.org/10.1109/SIU.2011.5929584 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12712/4592 | |
| dc.description | 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 -- 20 April 2011 through 22 April 2011 -- Antalya -- 85528 | en_US |
| dc.description.abstract | In this study, a new Support Vector Machine (SVM) based method for diagnosis of diabetes is proposed. In the proposed method, feature of adaptibility is added to the support vector machine. Thus, a new kind of SVM named "Adaptive SVM" is proposed, and smartly diagnosis of diseases is aimed. During the training and testing of this newly designed smart system, diabetes data set which is obtained from the medical database of University of California is used. It is observed that the classification rate of this newly proposed method on the diabetes data set is 100%. © 2011 IEEE. | en_US |
| dc.language.iso | tur | en_US |
| dc.relation.isversionof | 10.1109/SIU.2011.5929584 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.title | Diagnosis of diabetes by using adaptive SVM | en_US |
| dc.title.alternative | Uyarlanabi?li?r DVM yöntemi? kullanilarak şeker hastaliginin teşhi?s edi?lmesi? | en_US |
| dc.type | conferenceObject | en_US |
| dc.contributor.department | OMÜ | en_US |
| dc.identifier.startpage | 46 | en_US |
| dc.identifier.endpage | 49 | en_US |
| dc.relation.journal | 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
