Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/4443
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Anastasopoulos, Panagiotis Ch | en |
dc.contributor.author | Nicolaides, Kypros H. | en |
dc.contributor.author | Schizas, Christos N. | en |
dc.contributor.author | Neokleous, Kleanthis C. | en |
dc.contributor.author | Neocleous, Costas | - |
dc.contributor.other | Νεοκλέους, Κώστας | - |
dc.date.accessioned | 2012-05-11T05:28:44Z | en |
dc.date.accessioned | 2013-05-17T10:36:12Z | - |
dc.date.accessioned | 2015-12-09T12:22:52Z | - |
dc.date.available | 2012-05-11T05:28:44Z | en |
dc.date.available | 2013-05-17T10:36:12Z | - |
dc.date.available | 2015-12-09T12:22:52Z | - |
dc.date.issued | 2009 | en |
dc.identifier.citation | Proceedings of the International Joint Conference on Neural Networks IJCNN, 2009, Atlanta | en |
dc.identifier.isbn | 9781424435531 | en |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/4443 | - |
dc.description.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%. | en |
dc.format | en | |
dc.language.iso | en | en |
dc.rights | © 2009 IEEE. All rights reserved. | en |
dc.title | Neural networks to estimate the risk for preeclampsia occurrence | en |
dc.type | Conference Papers | en |
dc.collaboration | King’s College Hospital Medical School | - |
dc.collaboration | Cyprus University of Technology | - |
dc.collaboration | University of Cyprus | - |
dc.subject.category | Mechanical Engineering | - |
dc.country | Cyprus | - |
dc.country | United Kingdom | - |
dc.subject.field | Engineering and Technology | - |
dc.identifier.doi | 10.1109/IJCNN.2009.5178820 | en |
dc.dept.handle | 123456789/141 | en |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.grantfulltext | open | - |
crisitem.author.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Neural networks to estimate the risk.pdf | 195.33 kB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
50
21
checked on Nov 9, 2023
Page view(s) 20
506
Last Week
2
2
Last month
6
6
checked on Aug 28, 2024
Download(s) 50
903
checked on Aug 28, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License