Rule induction algorithm for brain glioma using support vector machine.
- Author:
Guozheng LI
1
;
Jie YANG
;
Jiaju WANG
;
Daoying GENG
Author Information
1. Institute of Image Processing Pattern Recognition, Shanghai Jiaotong University, Shanghai, China. gzli@staff.shu.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Brain Neoplasms;
pathology;
Diagnosis, Computer-Assisted;
methods;
Female;
Glioma;
pathology;
Humans;
Magnetic Resonance Imaging;
Male;
Models, Statistical;
Neural Networks (Computer);
Predictive Value of Tests
- From:
Journal of Biomedical Engineering
2006;23(2):410-412
- CountryChina
- Language:Chinese
-
Abstract:
A new proposed data mining technique, support vector machine (SVM), is used to predict the degree of malignancy in brain glioma. Based on statistical learning theory, SVM realizes the principle of data dependent structure risk minimization, so it can depress the overfitting with better generalization performance, since the prediction in medical diagnosis often deals with a small sample. SVM based rule induction algorithm is implemented in comparison with other data mining techniques such as artificial neural networks, rule induction algorithm and fuzzy rule extraction algorithm based on fuzzy max-min neural networks (FRE-FMMNN) proposed recently. Computation results by 10 fold cross validation method show that SVM can get higher prediction accuracy than artificial neural networks and FRE-FMMNN, which implies SVM can get higher accuracy and more reliability. On the whole data sets, SVM gets one rule with the classification accuracy of 89.29%, while FRE-FMMNN gets two rules of 84. 64%, in which the rule got by SVM is of quantity relation and contains more information than the two rules by FRE-FMMNN. All the above show SVM is a potential algorithm for the medical diagnosis such as the prediction of the degree of malignancy in brain glioma.
