A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine
A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine
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DOI：
10.1016/j.eswa.2006.09.012
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发表时间：
2008-01-01
影响因子：
8.5
通讯作者：
Arslan, Ahmet
中科院分区：
文献类型：
--
作者：
Polat, Kemal;Gunes, Salih;Arslan, Ahmet
本研究的目的是利用广义判别分析（GDA）和最小二乘支持向量机（LS-SVM）对医学领域中最重要的疾病之一糖尿病进行诊断。提出了一种基于广义判别分析和最小二乘支持向量机的级联学习系统。拟议的制度包括两个阶段。第一阶段，我们使用广义判别分析来判别健康和患者（糖尿病）数据之间的特征变量作为预处理过程。第二阶段，我们使用LS-SVM对糖尿病数据集进行分类。LS-SVM使用10倍交叉验证获得了78.21%的分类准确率，而GDA-LS-SVM使用10倍交叉验证获得了82.05%的分类准确率。使用分类精度，k折交叉验证方法和混淆矩阵检查所提出的系统的鲁棒性。所获得的分类准确率为82.05%，这是非常有前途的相比，以前报道的分类技术。(c)2006爱思唯尔有限公司版权所有。
The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques. (c) 2006 Elsevier Ltd. All rights reserved.