Neural networks prediction of preterm delivery with first trimester bleeding

• Published in: Archives of gynecology and obstetrics Vol. 283; no. 5; pp. 971 - 979
• Main Authors: Elaveyini, U., Devi, S. Prasanna, Rao, K. Suryaprakasa
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.05.2011; Springer Nature B.V
• 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; India; Neural networks; First trimester bleeding; Preterm labor prediction
• ISSN: 0932-0067; 1432-0711
• DOI: 10.1007/s00404-010-1469-2

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.