Neural networks prediction of preterm delivery with first trimester bleeding

Objective This paper illustrates a retrospective study of the outcome of those pregnancies that continued from an initial episode of bleeding in first trimester. Methods Neural networks is used for the prediction of preterm delivery, using various inputs such as the age of women, gestational age whe...

Full description

Saved in:
Bibliographic Details
Published in:Archives of gynecology and obstetrics 2011-05, Vol.283 (5), p.971-979
Main Authors: Elaveyini, U., Devi, S. Prasanna, Rao, K. Suryaprakasa
Format: Article
Language:English
Subjects:
Adult
Endocrinology
Female
Forecasting
Gynecology
Health risk assessment
Human Genetics
Humans
India - epidemiology
Materno-Fetal Medicine
Medicine
Medicine & Public Health
Neural networks
Neural Networks (Computer)
Obstetric Labor, Premature - epidemiology
Obstetrics/Perinatology/Midwifery
Pregnancy
Pregnancy Complications - epidemiology
Pregnancy Outcome
Pregnancy Trimester, First
Premature Birth - epidemiology
Retrospective Studies
Uterine Hemorrhage - epidemiology
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objective This paper illustrates a retrospective study of the outcome of those pregnancies that continued from an initial episode of bleeding in first trimester. Methods 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. Results 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.
ISSN:0932-0067
1432-0711
DOI:10.1007/s00404-010-1469-2