Abstract
As fetal hypoxia may damage or kill the fetus, it is very important to monitor the infant so that any signs of fetal distress can be detected as soon as possible. In this paper, the performances of some artificial neural networks are evaluated, which eventually produce the suggested diagnosis of fetal hypoxia. Multilayer perceptron (MLP) structure with standard back propagation, MLP with fast back propagation (adaptive learning and momentum term added), Radial Basis Function (RBF) network structure trained by orthogonal least square algorithm, and Conic Section Function Neural Network (CSFNN) with adaptive learning were used for this purpose. Further more, Receiver Operating Characteristic (ROC) analysis is used to determine the accuracy of diagnostic test.
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© 2004 Springer-Verlag Berlin Heidelberg
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Özyılmaz, L., Yıldırım, T. (2004). ROC Analysis for Fetal Hypoxia Problem by Artificial Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_160
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DOI: https://doi.org/10.1007/978-3-540-24844-6_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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