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TitlePredicting preterm birth in maternity care by means of data mining
Author(s)Pereira, Sónia
Portela, Filipe
Santos, Manuel Filipe
Machado, José Manuel
Abelha, António
KeywordsData mining
Preterm birth
Real data
Obstetrics care
Maternity care
Issue date2015
JournalProgress in Artificial Intelligence
Abstract(s)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.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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