| Home | E-Submission | Sitemap | Editorial Office |  
top_img
J Health Info Stat > Volume 38(1); 2013 > Article
J Health Info Stat 2013;38(1):108-122.
최적의 서포트 벡터 머신을 이용한 유방암 분류
임진수 , 손진영 , 손주태 , 임동훈
Breast Cancer Classification Using Optimal Support Vector Machine
Jin Soo Lim , Jin Young Sohn , Ju Tae Sohn , Dong Hoon Lim
ABSTRACT
Objectives:
This paper is to examine breast cancer classification using support vector machine (SVM). SVM with optimal parameters obtained using the improved grid search with 5-fold cross validation has been proposed to reach the optimal classification performance.
Methods:
Two data sets, Wisconsin Original Breast Cancer (WOBC) and Wisconsin Diagnostic Breast Cancer (WDBC) data set, were used to classify tumors as benign and malignant. SVM model performs the classification tasks using optimal kernel parameter and penalty parameter using 5-fold cross validation. Discriminant analysis, logistic regression analysis, decision tree, support vector machines were applied to analyze two data sets. Performance of these techniques was compared through accuracy, ROC curves and c-statistics.
Results:
Our analysis showed that SVMs predicted breast cancer with highest accuracy and c-statistics among four classification models. A comparison of these SVMs indicated that SVM with optimal parameters has much superior performance than SVM with default parameters.
Conclusions:
Research efforts have reported with increasing confirmation that SVMs have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on SVM with optimal parameters obtained using the improved grid search with 5-fold cross validation has been proposed. The performance of the method is evaluated using classification accuracy, ROC curves and c-statistics.
Key words: Classification, Breast cancer, Support vector machine, Performance evaluation, Optimal parameter
TOOLS
PDF Links  PDF Links
Full text via DOI  Full text via DOI
Download Citation  Download Citation
Share:      
METRICS
1,407
View
60
Download
Related article
Breast Cancer Classification using Deep Learning-based Ensemble  2018 May;43(2)
Editorial Office
The Korean Society of Health Informatics and Statistics
680 gukchaebosang-ro, Jung-gu, Daegu, 41944, Korea
E-mail: koshis@hanmail.net
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
Copyright © The Korean Society of Health Informatics and Statistics.                 Developed in M2PI