Title :
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
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;
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
Conference_Location :
Jalarta
Print_ISBN :
978-1-4673-1459-6
DOI :
10.1109/URKE.2012.6319555
