Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/9046
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dc.contributor.authorNeocleous, Andreas C.-
dc.contributor.authorNeocleous, Costas-
dc.contributor.authorPetkov, Nicolai-
dc.contributor.authorNicolaides, Kypros H.-
dc.contributor.authorSchizas, Christos N.-
dc.contributor.otherΝεοκλέους, Κώστας-
dc.date.accessioned2017-01-16T10:37:45Z-
dc.date.available2017-01-16T10:37:45Z-
dc.date.issued2016-04-01-
dc.identifier.citation14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016; Paphos; Cyprus; 31 March 2016 through 2 April 2016en_US
dc.identifier.isbn978-331932701-3-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/9046-
dc.description.abstractThe early detection of fetal chromosomal abnormalities such as aneuploidies, has been an important subject in medicine over the last thirty years. A pregnant woman is advised by the doctor to perform an amniocentesis test, after the identification of increased risk for fetal aneuploidy. Even though the amniocentesis test is almost perfectly accurate, it has several drawbacks. It is an invasive test with around 1% risk for miscarriage; it is financially expensive and requires laboratories and special equipment. In this work we propose a non-invasive method for aneuploidy detection using a dataset with pre-natal examinations of pregnant women and artificial neural networks. We have used a dataset with 50,517 euploid and 691 aneuploid cases. Biological markers of the mother such as the age, blood proteins and ultrasonographic information from the fetus are used as input to the networks. A training set is used to construct neural networks and a test set is used for validation. Each unknown case is assigned into a class between “euploid” and “aneuploid” using a cut-off value on the network output. We create a ROC curve by computing the sensitivity and the specificity for a set of different cut-off values. From the ROC curve, we indicate the importance of the cut-off values in terms of health economics and social affection. It is shown that by increasing the cut-off value, the false positive rate reduces with the cost of an increased false negative rate.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.rights© Springer International Publishing Switzerland 2016.en_US
dc.subjectArtificial neural networksen_US
dc.subjectChromosomal abnormalitiesen_US
dc.subjectComputational intelligenceen_US
dc.subjectNon-invasive diagnosisen_US
dc.subjectROC curveen_US
dc.titlePrenatal diagnosis of aneuploidy using artificial neural networks in relation to health economicsen_US
dc.typeConference Papersen_US
dc.doi10.1007/978-3-319-32703-7_181en_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationUniversity of Groningenen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationKing’s College Hospitalen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.countryNetherlandsen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
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-
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers
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