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%. 1. Introduction There is a growing tendency to use clinical decision support systems in medical diagnosis. These systems help to optimize medical decisions, improve medical treatments, and reduce financial costs [1, 2]. A large number of the medical diagnosis procedures can be converted into intelligent data classification tasks. These classification tasks can be categorized as two-class task and multiclass task. The first type separates the data between only two classes while the second type involves the classification of the data with more than two classes [3]. Cardiotocography was introduced into obstetrics practice in the early 1970s, and since then it has been used as a worldwide method for antepartum (before delivery) and intrapartum (during delivery) fetal monitoring. Cardiotocogram (CTG) is a recording of two distinct signals, fetal heart rate (FHR), and uterine activity (UA) [4]. It is used for determining the fetal state during both pregnancy and delivery. The aim of the CTG monitoring is to determine babies who may be short of oxygen (hypoxic); thus further assessments of fetal condition may be performed or the baby might be delivered by caesarean section or natural birth [5]. The visual evaluation of the CTG not only requires time but also depends on the knowledge and clinical experience of obstetricians. A clinical decision support system eliminates the inconsistency of visual evaluation. There have been proposed several classification tools for developing such system [4, 6–10]. One of these tools is support vector machine (SVM) and it is used in [4, 8, 10]. In [4, 8], SVM is used for FHR signal classification with two classes, normal or at risk. The risk of metabolic acidosis for newborn based on FHR signal is predicted in [4] while the classification of antepartum FHR signal is made in [8]. In [10], a medical decision support system based on SVM and genetic algorithm (GA) is
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