Abstract
The 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.
The original version of this chapter was inadvertently published with an incorrect chapter pagination 930–934 and DOI 10.1007/978-3-319-32703-7_181. The page range and the DOI has been re-assigned. The correct page range is 936–940 and the DOI is 10.1007/978-3-319-32703-7_182. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Nicolaides H. K, (2011) Screening for Fetal Aneuploidies at 11 to 13 Weeks. Prenat. Diag., vol. 31, No.2, pp. 7-15
Nicolaides H. K, Spencer K, Avgidou K, et al (2005) Multicenter Study of First-Trimester Screening for Trisomy 21 in 75821 Pregnancies: Results and Estimation of the Potential Impact of Individual Risk-Orientated Two-Stage First-Trimester Screening. Ultrasound Obstet. Gynecol. vol. 25, No. 3, pp. 221-226
Dugoff L, (2010) First-and Second-Trimester Maternal Serum Markers for Aneuploidy and Adverse Obstetric Outcomes. Amer. Coll. of Obst. And Gynecol. vol. 115, No. 5, pp. 1052-61
Neocleous C. A, Nicolaides H. K, Schizas N. C, (2015) First Trimester Non-invasive Prenatal Diagnosis: A Computational Intelligence Approach. IEEE Journal of Biomedical and Health Informatics
Bamber D, (1975) The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology vol. 12, pp. 387-415
Zweig H. M, Campbell G, (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry vol. 39, pp. 561-577
Greiner M, Pfeiffer D, Smith D, (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine vol. 45 pp. 23-41
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Neocleous, A.C., Neocleous, C.K., Petkov, N., Nicolaides, K.H., Schizas, C.N. (2016). Prenatal Diagnosis of Aneuploidy Using Artificial Neural Networks in Relation to Health Economics. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_182
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_182
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)