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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorNeocleous, Andreas C.en
dc.contributor.authorNicolaides, Kypros H.en
dc.contributor.authorNeocleous, Costas-
dc.contributor.otherΝεοκλέους, Κώστας-
dc.date.accessioned2013-03-04T12:43:19Zen
dc.date.accessioned2013-05-17T10:38:45Z-
dc.date.accessioned2015-12-09T12:04:21Z-
dc.date.available2013-03-04T12:43:19Zen
dc.date.available2013-05-17T10:38:45Z-
dc.date.available2015-12-09T12:04:21Z-
dc.date.issued2012en
dc.identifier.citationArtificial intelligence applications and innovations: AIAI 2012 international workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II, Pages 46-55en
dc.identifier.isbn978-3-642-33411-5 (print)en
dc.identifier.isbn978-3-642-33412-2 (online)en
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4282-
dc.description.abstractA selection of artificial neural network models were built and implemented for systematically study the contribution and the sensitivity of the main influencing parameters as important contributing factors for the non-invasive prediction of chromosomal abnormalities. The parameters that had been investigated are: the previous medical history of the pregnant mother, the nasal bone, the tricuspid flow, the ductus venosus flow, the PAPP-A value, the b-hCG value, the crown rump length (CRL), the changes in nuchal translucency (deltaNT) and the mother’s age. The main conclusions drawn are: 1) The deltaNT is the most significant factor for the overall prediction, while the CRL the least significant. 2) The previous medical history of the pregnant mother is not a significant factor for the prediction of the abnormal cases. 3) The nasal bone, the tricuspid flow and the ductus venosus flow contribute significantly in the prediction of trisomy 21 but not in the prediction of the “normal” cases. 4) The PAPP-A, the b-hCG and the mother’s age are of intermediate importance. Also, a sensitivity analysis of the attributes PAPP-A, b-hCG, CRL, deltaNT and of the mother’s age was done. This analysis showed that the CRL and deltaNT are more sensitive when their values are decreased, the PAPP-A is more sensitive when its values are increased and the b-hCG is insensitive to variations in its valuesen
dc.language.isoenen
dc.rights© 2012 IFIP International Federation for Information Processingen
dc.subjectNeural networks (Computer science)en
dc.subjectForecastingen
dc.subjectBoneen
dc.titleArtificial neural networks to investigate the importance and the sensitivity to various parameters used for the prediction of chromosomal abnormalitiesen
dc.typeBook Chapteren
dc.collaborationUniversity of Cyprus-
dc.collaborationKing’s College Hospital Medical School-
dc.collaborationCyprus University of Technology-
dc.subject.categoryElectrical engineering,Electrinic engineering,Information engineering-
dc.reviewpeer reviewed-
dc.countryCyprus-
dc.countryUnited Kingdom-
dc.subject.fieldEngineering and Technology-
dc.identifier.doi10.1007/978-3-642-33412-2_5en
dc.dept.handle123456789/134en
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.cerifentitytypePublications-
item.openairetypebookPart-
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-
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