Result: ROC analysis for fetal hypoxia problem by artificial neural networks

Title:
ROC analysis for fetal hypoxia problem by artificial neural networks
Authors:
ÖZYILMAZ, Lale, YILDIRIM, Tülay
Source:
Artificial intelligence and soft computing (Zakopane, 7-11 June 2004)Lecture notes in computer science. :1026-1030
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 6 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Diagnostic, Diagnosis, Diagnóstico, Endommagement, Damaging, Deterioración, Fonction base radiale, Radial basis function, Función radial base, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Méthode adaptative, Adaptive method, Método adaptativo, Méthode moindre carré, Least squares method, Método cuadrado menor, Perceptron multicouche, Multilayer perceptrons, Réseau multicouche, Multilayer network, Red multinivel, Réseau neuronal, Neural network, Red neuronal, Structure réseau, Network structure, Conique, Conics, Cónico
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electronics & Comm. Eng., Yildiz Technical University, Istanbul 34349, Turkey
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=15852257
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.15852257
Database:
PASCAL Archive

Further Information

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.