Myocardial ınfarction classification with support vector machine models

Basit Öğe Kaydı
dc.authorid105641en_US
dc.authorid103692en_US
dc.authorid39164en_US
dc.authorid9712en_US
dc.contributor.authorGüldoğan, Emek
dc.contributor.authorYağmur, Jülide
dc.contributor.authorYoloğlu, Saim
dc.contributor.authorAsyalı, Musa Hakan
dc.contributor.authorÇolak, Cemil
dc.date.accessioned2017-12-22T07:17:54Z
dc.date.available2017-12-22T07:17:54Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.descriptionJ Turgut Ozal Med Cent, 22(4), 221–224.en_US
dc.description.abstractAim: Support vector machines (SVM) is one of the classification methods that aims to find the best hyper-plane separating a space into two parts with known positive and negative samples. The goal of this study is to classify myocardial infarction (MI) using SVM models. Material and Methods: The data used in the MI classification contains information related to 184 individuals which is randomly taken from the database created for the Department of Cardiology, Faculty of Medicine, Inonu University. Estimated SVMs are models generated from the SVM-linear and SVM-Radial Based kernel functions. Results: In this study, 90 individuals of the study group (48.9%) are MI patients, while 94 (51.1%) patients are not. The classification success rate is 83.70% for SVM-linear model and 90.76% for the SVM-Radial Based model. Conclusion: In this study, it is observed that SVM-Radial based model presented a better classification performance than the linear SVM model. The use of SVM models based on various kernel type functions can improve disease classification performance.en_US
dc.identifier.citationGüldoğan, E., Yağmur, J., Yoloğlu, S., Asyalı, M. H., & Çolak, C. (2015). Myocardial Infarction Classification With Support Vector Machine Models. J Turgut Ozal Med Cent, 22(4), 221–224.en_US
dc.identifier.doi10.7247/jtomc.2015.2671en_US
dc.identifier.endpage224en_US
dc.identifier.issn1300-1744
dc.identifier.issue4en_US
dc.identifier.startpage221en_US
dc.identifier.trdizinid200663en_US
dc.identifier.urihttps://doi.org/10.7247/jtomc.2015.2671
dc.identifier.urihttps://hdl.handle.net/11616/7920
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/200663
dc.identifier.volume22en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherJ Turgut Ozal Med Centen_US
dc.relation.ispartofJ Turgut Ozal Med Centen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectMyocardial Infarctionen_US
dc.subjectClassificationen_US
dc.titleMyocardial ınfarction classification with support vector machine modelsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Makale Dosyası.pdf
Boyut:
618.34 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.71 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu