Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree

Abstract

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%.

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

“…Recent research has done [53] using hybrid model. The model suggests using LS-SVM and PSO (particle swarm optimization) to train the CTG data after getting trained data again applying the same procedure then in the last step applying Binary Decision Tree to get classified data.…”

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

“…Huang and Hsu [7] have offered discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. Yılmaz and Kılıkçıer [8] have suggested using least square (LS) support vector machine (SVM) with particle swarm optimization and binary DT. * corresponding author; e-mail: comertzafer@gmail.com Ocak [9] has developed a medical decision support system based on SVM and genetic algorithm (GA).…”

Binary tree optimization using genetic algorithm for multiclass support vector machine

“…SVM classifies a sample as a positive or negative Vapnik, 2000). However, most problems in the real world are usually multiclass problems (Du, Liu, & Xi, 2015;Huang, Zhang, Zeng, & Bushel, 2013;Pathak & Sunkaria, 2014;YJlmaz & KJlJkçJer, 2013). For multiclass problems, there are more than two classes, and a sample is classified as one class among many classes.…”