Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/5337
Title: Neural networks to estimate the risk for preeclampsia occurrence
Authors: Anastasopoulos, Panagiotis Ch
Nicolaides, Kypros H.
Schizas, Christos N.
Neokleous, Kleanthis C.
Neocleous, Costas 
Category: Mechanical Engineering
Field: Engineering and Technology
Issue Date: 2009
Publisher: IEEE
Source: Proceedings of the International Joint Conference on Neural Networks IJCNN, 2009, Atlanta
Abstract: A number of neural network schemes have been applied to a large data base of pregnant women, aiming at generating a predictor for the risk of preeclampsia occurrence at an early stage. The database was composed of 6838 cases of pregnant women in UK, provided by the Harris Birthright Research Centre for Fetal Medicine in London. For each subject, 24 parameters were measured or recorded. Out of these, 15 parameters were considered as the most influencing at characterizing the risk of preeclampsia occurrence. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. The best results obtained were with a multi-slab neural structure. In the training set there was a correct classification of the 83.6% cases of preeclampsia and in the test set 93.8%. The preeclampsia cases prediction for the totally unknown verification test was 100%.
URI: http://ktisis.cut.ac.cy/handle/10488/5337
ISBN: 9781424435531
DOI: 10.1109/IJCNN.2009.5178820
Rights: © 2009 IEEE. All rights reserved.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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