最小二乘支持向量机在医疗数据分析中的应用

Application of Least Squares Support Vector Machines to Medical Diagnostics

以医疗数据为应用对象,应用网格搜索和交叉验证的方法选择参数,建立最小二乘支持向量机分类器,进行实际验证,并与使用K近邻分类器(K-NN)和C4.5决策树两种方法的结果进行比较。结果表明,LS-SVM分类器取得较高的准确率,表明最小二乘支持向量机在医疗诊断研究中具有很大的应用潜力。

As the medical diagnose dada an application object, LS-SVM classification hyper-parameters are optimized with grid-search and cross-validation method, carry through to validate the classification performance. Then it is compared to other typical classifications such as K-NN and C4.5 decision tree on the datasets. Computational results indicate that LS-SVM has good performance on the classification recognize, LS-SVM has potential application in medical diagnostics research.