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|Title: ||Artificial neural networks for non-invasive chromosomal abnormality screening of fetuses|
|Authors: ||Neocleous, Costas|
Nicolaides, Kypros H.
Neokleous, Kleanthis C.
Schizas, Christos N.
|???metadata.dc.subject.field???: ||Medical and Health Sciences|
|???metadata.dc.subject.category???: ||Clinical Medicine|
|Keywords: ||Chromosomal abnormalities|
Neural network structures
|Issue Date: ||2010|
|Citation: ||6th IEEE World Congress on Computational Intelligence, WCCI, 2010, Barcelona, Spain.|
|Abstract: ||A large number of different neural network structures
have been constructed, trained and tested to a large data
base of pregnant women characteristics, aiming at generating a
classifier-predictor for the presence of chromosomal abnormalities
in fetuses, namely the Trisomy 21 (Down syndrome),
Trisomy 18 (Edwards syndrome), Trisomy 13 (Patau syndrome)
and the Turner syndrome.
The database was composed of 31611 cases of pregnant
women. 31135 women did not show any chromosomal abnormalities,
while the remaining 476 were confirmed as having a
chromosomal anomaly of T21, T18, T13, or Turner Syndrome.
From the total of 31611 cases, 8191 were kept as a totally
unknown database that was only used for the verification of the
predictability of the network. In this set, 7 were of the Turner
syndrome, 14 of the Patau syndrome, 42 of the Edwards syndrome
and 71 of the Down syndrome.
For each subject, 10 parameters were considered to be the
most influential at characterizing the risk of occurrence of
these types of chromosomal anomalies.
The best results were obtained when using a multi-layer neural
structure having an input, an output and three hidden layers.
For the case of the totally unknown verification set of the
8191 cases, 98.1% were correctly identified. The percentage of
abnormal cases correctly predicted was 85.1%. The unknown
T21 cases were predicted by 78.9%, the T18 by 76.2%, the T13
by 0.0% and the Turner syndrome by 42.9%.|
|???metadata.dc.type.*???: ||Conference Papers|
|???metadata.dc.rights???: ||© 2010 IEEE. All rights reserved.|
|???metadata.dc.subject.collaboration???: ||Cyprus University of Technology|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια / Conference papers|
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