Breast cancer diagnosis using least square support vector machine
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ACADEMIC PRESS INC ELSEVIER SCIENCE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
breast cancer diagnosis, Wisconsin breast cancer diagnosis data, least square support vector machine, confusion matrix, k-fold cross validation, medical diagnosis
Kaynak
DIGITAL SIGNAL PROCESSING
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
17
Sayı
4
