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|Title:||Artificial neural networks to investigate the importance and the sensitivity to various parameters used for the prediction of chromosomal abnormalities||Authors:||Neocleous, Andreas C.
Nicolaides, Kypros H.
|Keywords:||Neural networks (Computer science);Forecasting;Bone||Category:||Electrical engineering,Electrinic engineering,Information engineering||Field:||Engineering and Technology||Issue Date:||2012||Publisher:||Springer||Source:||Artificial 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 46-55||Abstract:||A 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 values||URI:||http://ktisis.cut.ac.cy/handle/10488/7556||ISBN:||978-3-642-33411-5 (print)
|DOI:||10.1007/978-3-642-33412-2_5||Rights:||© 2012 IFIP International Federation for Information Processing||Type:||Book Chapter|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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