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TitlePredicting type of delivery by identification of obstetric risk factors through data mining
Author(s)Pereira, Sónia
Portela, Filipe
Santos, Manuel Filipe
Machado, José Manuel
Abelha, António
KeywordsData mining
Type of delivery
Real data
Obstetrics care
Maternity care
Real data;Obstetrics Care
Issue date2015
JournalProcedia Computer Science
Abstract(s)In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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