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https://hdl.handle.net/20.500.14279/4443
Τίτλος: | Neural networks to estimate the risk for preeclampsia occurrence | Συγγραφείς: | Anastasopoulos, Panagiotis Ch Nicolaides, Kypros H. Schizas, Christos N. Neokleous, Kleanthis C. Neocleous, Costas |
metadata.dc.contributor.other: | Νεοκλέους, Κώστας | Major Field of Science: | Engineering and Technology | Field Category: | Mechanical Engineering | Ημερομηνία Έκδοσης: | 2009 | Πηγή: | Proceedings of the International Joint Conference on Neural Networks IJCNN, 2009, Atlanta | Περίληψη: | A number of neural network schemes have been applied to a large data base of pregnant women, aiming at generating a predictor for the risk of preeclampsia occurrence at an early stage. The database was composed of 6838 cases of pregnant women in UK, provided by the Harris Birthright Research Centre for Fetal Medicine in London. For each subject, 24 parameters were measured or recorded. Out of these, 15 parameters were considered as the most influencing at characterizing the risk of preeclampsia occurrence. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. The best results obtained were with a multi-slab neural structure. In the training set there was a correct classification of the 83.6% cases of preeclampsia and in the test set 93.8%. The preeclampsia cases prediction for the totally unknown verification test was 100%. | URI: | https://hdl.handle.net/20.500.14279/4443 | ISBN: | 9781424435531 | DOI: | 10.1109/IJCNN.2009.5178820 | Rights: | © 2009 IEEE. All rights reserved. | Type: | Conference Papers | Affiliation: | King’s College Hospital Medical School Cyprus University of Technology University of Cyprus |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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Neural networks to estimate the risk.pdf | 195.33 kB | Adobe PDF | Δείτε/ Ανοίξτε |
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