title: 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%.
authors.affiliation: Electrical-Electronic Engineering Department, Uludag University, 16059 Gorukle, Bursa, Turkey.
authors.forename: Ersen
authors.initials: E
authors.lastname: Yılmaz
authors.forename: Cağlar
authors.initials: C
authors.lastname: Kılıkçıer
mesh_terms: D001185:Artificial Intelligence; D015148:Cardiotocography / Q000706:statistics & numerical data*; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D011247:Pregnancy; D012372:ROC Curve; D060388:Support Vector Machine