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dc.contributor.authorNeocleous, Costas K.en
dc.contributor.authorNicolaides, Kypros H.en
dc.contributor.authorNeokleous, Kleanthis C.en
dc.contributor.authorSchizas, Christos N.en
dc.creatorNeocleous, Costas K.en
dc.creatorNicolaides, Kypros H.en
dc.creatorNeokleous, Kleanthis C.en
dc.creatorSchizas, Christos N.en
dc.date.accessioned2019-11-13T10:41:24Z
dc.date.available2019-11-13T10:41:24Z
dc.date.issued2010
dc.identifier.issn0302-9743
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54585
dc.description.abstractA large number of feedforward neural structures, both standard multilayer and multi-slab 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. In this study we have investigated the importance of ethnicity on the classification yield. 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 15 parameters were considered as the most influential at characterizing the risk of preeclampsia occurrence, including information on ethnicity. The same data were applied to the same neural architecture, after excluding the information on ethnicity, in order to study its importance on the correct classification yield. It has been found that the inclusion of information on ethnicity, deteriorates the prediction yield in the training and test (guidance) data sets but not in the totally unknown verification data set. © Springer-Verlag Berlin Heidelberg 2010.en
dc.source6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications, SETN 2010en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-78650435003&doi=10.1007%2f978-3-642-12842-4_49&partnerID=40&md5=c621a98ae0ce9c816afe9dde8c0b015b
dc.subjectRisk perceptionen
dc.subjectStatistical testsen
dc.subjectNeural networksen
dc.subjectFeed-Forwarden
dc.subjectData setsen
dc.subjectEthnicityen
dc.subjectPreeclampsiaen
dc.subjectLarge dataen
dc.subjectNeural structuresen
dc.subjectPregnant womanen
dc.subjectResearch centresen
dc.subjectGestational ageen
dc.subjectNeural architecturesen
dc.subjectNeural predictoren
dc.subjectNeural predictorsen
dc.subjectVerification dataen
dc.titleEthnicity as a factor for the estimation of the risk for preeclampsia: A neural network approachen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/978-3-642-12842-4_49
dc.description.volume6040 LNAIen
dc.description.startingpage395
dc.description.endingpage398
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.source.abbreviationLect. Notes Comput. Sci.en
dc.contributor.orcidSchizas, Christos N. [0000-0001-6548-4980]
dc.gnosis.orcid0000-0001-6548-4980


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