Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree
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Date
2013
Authors
Journal Title
Journal ISSN
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Publisher
Hindawi
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%.
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Keywords
Mathematical & computational biology, Heart-rate, Classification, Performance, System, Risk, Binary trees, Decision trees, Particle swarm optimization (PSO), 10-fold cross-validation, Binary decision trees, Cardiotocogram, Classification accuracy, Least squares support vector machines, Operation characteristic, Support vector machines
Citation
Yilmaz, E. ve Kılıkçıer, Ç. (2013). "Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree". Computational and Mathematical Methods in Medicine, 2013.