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Predicting Preterm Birth in Maternity Care by Means of Data Mining

  • Sónia Pereira
  • Filipe PortelaEmail author
  • Manuel F. Santos
  • José Machado
  • António Abelha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9273)

Abstract

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Keywords

Data mining Preterm birth Real data Obstetrics care Maternity care 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sónia Pereira
    • 1
  • Filipe Portela
    • 1
    Email author
  • Manuel F. Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  1. 1.Algoritmi CentreUniversity of MinhoBragaPortugal

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