Electrical-Electronic Engineering Department, Uludag University, 16059 Gorukle, Bursa, Turkey.
Comput Math Methods Med. 2013;2013:487179. doi: 10.1155/2013/487179. Epub 2013 Oct 29.
We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.
我们使用最小二乘支持向量机（LS-SVM）结合二叉决策树对胎心率进行分类，以判断胎儿的状态。LS-SVM 的参数通过粒子群优化进行优化。通过 10 折交叉验证来检验方法的稳健性。该方法的性能通过整体分类准确率进行评估。此外，还呈现了接收者操作特征分析和蛛网图表示，以分析和可视化该方法的性能。实验结果表明，所提出的方法实现了高达 91.62%的出色分类准确率。
