Breast cancer diagnosis based on support vector machine

Author

Gao, Shang ; Li, Hongmei

Author_Institution

Sch. of Comput. Sci. & Technol., Jiangsu Univ. of Sci. & Technol., Zhenjiang, China

fYear

2012

fDate

14-15 Aug. 2012

Firstpage

240

Lastpage

243

Abstract

There are some problems still exist in traditional individual Breast Cancer Diagnosis. To solve the problems, an individual credit assessment model based on support vector classification method is proposed. Using SPSS Clementine data mining tool, the personal credit data is clustering analysis by Support Vector Machine. It is analyzed in detail with the different kernel functions and parameters of Support vector machine. Support vector machine could be used to improve the work of medical practitioners in the diagnosis of breast cancer.

Keywords

cancer; data mining; medical computing; patient diagnosis; pattern classification; pattern clustering; support vector machines; SPSS Clementine data mining tool; breast cancer diagnosis; clustering analysis; individual credit assessment model; kernel functions; medical practitioners; personal credit data; support vector classification method; support vector machine; Breast cancer; Computational modeling; Educational institutions; Kernel; Support vector machines; Training; breast cancer diagnosis; kernel function; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on

Conference_Location

Jalarta

Print_ISBN

978-1-4673-1459-6

Type

conf

DOI

10.1109/URKE.2012.6319555

Filename

6319555