ROC analysis for fetal hypoxia problem by artificial neural networks

Özyilmaz L., YILDIRIM T.

7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, Zakopane, Polonya, 7 - 11 Haziran 2004, cilt.3070, ss.1026-1030, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 3070
  • Doi Numarası: 10.1007/978-3-540-24844-6_160
  • Basıldığı Şehir: Zakopane
  • Basıldığı Ülke: Polonya
  • Sayfa Sayıları: ss.1026-1030
  • İstanbul Gelişim Üniversitesi Adresli: Hayır

Özet

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.