Diagnosis of Breast Cancer Tumor Based on ICA and LS-SVM

Author

Wang, Chao-yong ; Wu, Chun-Guo ; Liang, Yan-Chun ; Guo, Xin-Chen

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2565

Lastpage

2570

Abstract

An efficient method for the diagnosis of breast cancer tumor is proposed based on independent component analysis (ICA) and least square support vector machine (LS-SVM). In order to save the expense of detection, firstly, variables are selected based on the theory of statistics. Then the ICA is introduced in a concise way and followed by extracting the ICA component from these selected variables. Finally the processed data are classified by the LS-SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method is superior to the classical BP algorithm

Keywords

cancer; independent component analysis; least squares approximations; medical diagnostic computing; statistical analysis; support vector machines; tumours; breast cancer tumor; independent component analysis; least square support vector machine; medical diagnostic computing; statistical theory; Breast cancer; Breast neoplasms; Breast tumors; Educational technology; Independent component analysis; Knowledge engineering; Laboratories; Least squares methods; Machine learning; Support vector machine classification; Support vector machines; Breast cancer tumor; Independent Component Analysis; Least Square Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258850

Filename

4028496