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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorAnastasopoulos, Panagiotis Chen
dc.contributor.authorNicolaides, Kypros H.en
dc.contributor.authorSchizas, Christos N.en
dc.contributor.authorNeokleous, Kleanthis C.en
dc.contributor.authorNeocleous, Costas-
dc.contributor.otherΝεοκλέους, Κώστας-
dc.date.accessioned2012-05-11T05:28:44Zen
dc.date.accessioned2013-05-17T10:36:12Z-
dc.date.accessioned2015-12-09T12:22:52Z-
dc.date.available2012-05-11T05:28:44Zen
dc.date.available2013-05-17T10:36:12Z-
dc.date.available2015-12-09T12:22:52Z-
dc.date.issued2009en
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks IJCNN, 2009, Atlantaen
dc.identifier.isbn9781424435531en
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4443-
dc.description.abstractA 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.formatpdfen
dc.language.isoenen
dc.rights© 2009 IEEE. All rights reserved.en
dc.titleNeural networks to estimate the risk for preeclampsia occurrenceen
dc.typeConference Papersen
dc.collaborationKing’s College Hospital Medical School-
dc.collaborationCyprus University of Technology-
dc.collaborationUniversity of Cyprus-
dc.subject.categoryMechanical Engineering-
dc.countryCyprus-
dc.countryUnited Kingdom-
dc.subject.fieldEngineering and Technology-
dc.identifier.doi10.1109/IJCNN.2009.5178820en
dc.dept.handle123456789/141en
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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