Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7593
Title: Neural networks to investigate the effects of smoking and alcohol abuse on the risk for preeclampsia
Authors: Nicolaides, Kypros H.
Neokleous, Kleanthis C.
Neocleous, Costas 
Keywords: Neural networks (Computer science)
Information technology
Preeclampsia
Risk management
Diagnostic imaging
Issue Date: 2009
Publisher: IEEE Xplore
Source: 9th international conference on information technology and applications in biomedicine, 2009. ITAB 2009, Pages 1-5
Abstract: Following the application of a large number of neural network schemes that 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, we investigated the importance of the parameters of smoking and alcohol intake on the classification yield. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. 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 influential at characterizing the risk of preeclampsia occurrence, including the characteristics on whether the pregnant woman was an active smoker or not, and on whether she was consuming alcohol. The same data were applied to the same neural architecture, after excluding the information on smoking and alcohol, in order to study the importance of these two parameters on the correct classification yield. It has been found that both information parameters, were needed in order to achieve a correct classification as high as 83.6% of preeclampsia cases in the whole dataset, and of 93.8% in the test set. The preeclampsia cases prediction, for the totally unknown verification test, was 100%. When information on smoking and alcohol intake were not used, the results deteriorated significantly
URI: http://ktisis.cut.ac.cy/handle/10488/7593
ISBN: 978-1-4244-5379-5 (print)
978-1-4244-5379-5 (online)
DOI: 10.1109/ITAB.2009.5394421
Rights: © 2009 IEEE
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

Show full item record

Page view(s)

18
Last Week
1
Last month
3
checked on Aug 17, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.