基于人体血液常/微量元素含量的SVM癌症辅助诊断  

SVM-aided Cancer Diagnosis Based on the Concentration of the Macroelement and Microelement in Human Blood

作　　者：袁前飞[1] 蔡从中[1] 肖汉光[1] 刘兴华[1] 温玉锋[1] 孔春阳[2] 

机构地区：[1]重庆大学应用物理系,重庆400044 [2]重庆师范大学物理学与信息技术学院,重庆400047

出　　处：《生物医学工程学杂志》2007年第3期513-518,共6页Journal of Biomedical Engineering

基　　金：重庆大学与新加坡国立大学国际联合科研资助项目(ARF-151-000-014-112);重庆市自然科学基金资助项目(CSTC;2006BB5240);重庆大学基础及应用基础研究基金资助项目(71341103)

摘　　要：支持向量机(Support vector machine,SVM)分类方法在实际二类分类问题的应用中显示出良好的学习和泛化能力,已被广泛地应用于许多研究领域。我们以癌症病人血液中6种元素(Ba,Ca,Cu,Mg,Se,Zn)的含量为研究对象,将SVM、最近邻法、决策树C4.5及人工神经网络等方法用于癌症病人和正常人的分类研究。研究表明:除C4.5的分类准确率保持不变之外,对数据的归一化处理能够提高SVM、KNN、ANN的分类效果。当使用线性核函数时,SVM通过5次交叉验证的最优平均分类准确率达到了95.95%,优于KNN(93.24%)、C4.5(79.93%)及ANN(94.59%)等分类器,表明该方法有望成为一种实用的癌症临床辅助诊断手段。Support vector machine （SVM） has shown its excellent learning and generalization ability for the binary classification of real problems and has been extensively employed in many areas. In this paper, SVM, K-Nearest Neighbor, Decision Tree C4.5 and Artificial Neural Network were applied to identify cancer patients and normal individuals using the concentrations of 6 elements including macroelements （Ca, Mg） and microelements （Ba, Cu, Se, Zn） in human blood. It was demonstrated, by using the normalized features instead of the original features, the classification performances can be improved from 91.89% to 95.95%, from 83.78% to 93.24%, and from 90. 54% to 94.59% for SVM, K-NN and ANN respectively, whereas that of C4.5 keeps unchangeable. The best average accuracy of SVM with linear dot kernel by using 5-fold cross validation reaches 95-95%, and is superior to those of other classifiers based on K-NN （93.24%）, C4.5 （79.73%）, and ANN （94.59%）. The study suggests that support vector machine is capable of being used as a potential application methodology for SVM-aided clinical cancer diagnosis.

关 键 词：支持向量机 常量元素 微量元素 癌症诊断 

分 类 号：R730.4[医药卫生—肿瘤]