DocumentCode :
1733815
Title :
Breast cancer classification by using support vector machines with reduced dimension
Author :
Mert, Ahmet ; Kilic, Niyazi ; Akan, Aydin
Author_Institution :
Dept. of Navig. Eng., Piri Reis Univ., Istanbul, Turkey
fYear :
2011
Firstpage :
37
Lastpage :
40
Abstract :
Correct and timely diagnosis of diseases is an essential matter in medical field. Limited human capability and limitations decrease the rate of correct diagnosis. Machine learning algorithms such as support vector machine (SVM) can help physicians to diagnose more correctly. In this study, Wisconsin diagnostic breast cancer (WDBC) data set is used to classify tumors as benign and malignant. Independent component analysis (ICA) is used to reduce the dimensionality of WDBC data into two feature vectors. The effect of using two reduced features to classify breast cancer with SVM and polynomial or radial basis function (RBF) kernels are investigated. Performances of these classifiers are evaluated to find out accuracy, sensitivity and specificity. In addition, the receiver operating characteristics (ROC) curves of SVM with these kernels are presented. Results show that SVM with quadratic kernel provides the most accurate diagnosis results (94.40%) and decreases the accuracy and sensitivity values slightly when the dimensionality is reduced into two feature vector computing two independent components.
Keywords :
cancer; independent component analysis; medical computing; patient diagnosis; pattern classification; support vector machines; SVM; WDBC; Wisconsin diagnostic breast cancer; breast cancer classification; feature vectors; independent component analysis; quadratic kernel; radial basis function; receiver operating characteristics; support vector machines; Accuracy; Breast cancer; Kernel; Sensitivity; Support vector machine classification; Breast cancer classification; Independent component analysis; ROC curve; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ELMAR, 2011 Proceedings
Conference_Location :
Zadar
ISSN :
1334-2630
Print_ISBN :
978-1-61284-949-2
Electronic_ISBN :
1334-2630
Type :
conf
Filename :
6044334
Link To Document :
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