Neural networks prediction of preterm delivery with first trimester bleeding
Elaveyini, U.; Devi, S.P.; Rao, K.S.
Archives of Gynecology and Obstetrics 283(5): 971-979
2011
ISSN/ISBN: 1432-0711 PMID: 20449599 DOI: 10.1007/s00404-010-1469-2Document Number: 219766
This paper illustrates a retrospective study of the outcome of those pregnancies that continued from an initial episode of bleeding in first trimester. Neural networks is used for the prediction of preterm delivery, using various inputs such as the age of women, gestational age when the bleeding occurred, duration of the bleeding days, amount of bleeding, number of episodes, presence or absence of hematoma and placentation position. The success rate of prediction obtained using the feed forward backpropogation network is 70%. Hence, this model can be used to identify women at the risk of premature delivery for planning antenatal care and clinical interventions in pregnancy.
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