Breast cancer diagnosis using least square support vector machine
| dc.contributor.author | Polat, Kemal | |
| dc.contributor.author | Guenes, Salih | |
| dc.date.accessioned | 2020-03-26T17:17:04Z | |
| dc.date.available | 2020-03-26T17:17:04Z | |
| dc.date.issued | 2007 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description.abstract | The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system. (c) 2006 Elsevier Inc. All rights reserved. | en_US |
| dc.identifier.doi | 10.1016/j.dsp.2006.10.008 | en_US |
| dc.identifier.endpage | 701 | en_US |
| dc.identifier.issn | 1051-2004 | en_US |
| dc.identifier.issue | 4 | en_US |
| dc.identifier.scopusquality | Q2 | en_US |
| dc.identifier.startpage | 694 | en_US |
| dc.identifier.uri | https://dx.doi.org/10.1016/j.dsp.2006.10.008 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/21250 | |
| dc.identifier.volume | 17 | en_US |
| dc.identifier.wos | WOS:000247899300002 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | en_US |
| dc.relation.ispartof | DIGITAL SIGNAL PROCESSING | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | breast cancer diagnosis | en_US |
| dc.subject | Wisconsin breast cancer diagnosis data | en_US |
| dc.subject | least square support vector machine | en_US |
| dc.subject | confusion matrix | en_US |
| dc.subject | k-fold cross validation | en_US |
| dc.subject | medical diagnosis | en_US |
| dc.title | Breast cancer diagnosis using least square support vector machine | en_US |
| dc.type | Article | en_US |
