Escalante H.J.Montes-y-Gomez M.Segura A.de Dios Murillo J.2020-03-262020-03-262016Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 259-270978331947954503029743https://hdl.handle.net/20.500.12585/8999Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In response to the above, this work proposes using several machine learning techniques, which are considered most relevant in a bio-medical setting, in order to predict the risk level for Severe Maternal Morbidity in patients during pregnancy. The population studied correspond to pregnant women receiving prenatal care and final attention at E.S.E Clínica de Maternidad Rafael Calvo in Cartagena, Colombia. This paper presents the preliminary results of an ongoing project, as well as methods and materials considered for the construction of the learning models. © Springer International Publishing AG 2016.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1007/978-3-319-47955-2_22Logistic regressionMachine learningSevere maternal morbidityArtificial intelligenceDiseasesLearning algorithmsObstetricsEarly predictionLearning modelsLogistic regressionsMachine learning techniquesMaternal morbidityMethods and materialsPregnant womanPublic health issuesLearning systemsinfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB57203489577571918358395719184419226325154200