Gestatsioondiabeedi ja makrosoomia prognoosimine ning nende riskitegurite analüüs masinõppe meetoditega

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dc.contributor.advisorLaur, Sven, juhendaja
dc.contributor.advisorRull, Kristiina, juhendaja
dc.contributor.authorPihu, Silvia
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-11-08T12:03:12Z
dc.date.available2023-11-08T12:03:12Z
dc.date.issued2020
dc.description.abstractLarge-for-gestational-age (LGA) may cause problems for both baby and mother during delivery, therefore the best solution is to predict and avoid it (by diet, cure of GDM, etc.) or at least use planned Caesarian section. Gestational diabetes (GDM) is known as a major risk factor for too large baby. Different machine learning algorithms were used to predict GDM and LGA on Estonian pregnancies and newborn data from 2012 to 2018 (4787 cases), using their risk factors. The best results were obtained by random forest method (AUC for GDM 0.96 and for LGA 0,92). The major risk factors for LGA occurred to be GDM and its correct diagnosing, the body mass index of the mother (before pregnancy), having large baby in previous pregnancy, the age of mother and the blood sugar level registered at the beginning of pregnancy.et
dc.identifier.urihttps://hdl.handle.net/10062/94103
dc.language.isoestet
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectlarge for gestational ageet
dc.subjectmacrosomiaet
dc.subjectgestational diabetes mellituset
dc.subjectmachine learninget
dc.subjectdata mininget
dc.subjectbinary classificationet
dc.subjectfeature selection CERCS: P160 Statisticset
dc.subjectoperation researchet
dc.subjectprogramminget
dc.subjectactuarial mathematicset
dc.subjectB570et
dc.subject.othermagistritöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titleGestatsioondiabeedi ja makrosoomia prognoosimine ning nende riskitegurite analüüs masinõppe meetoditegaet
dc.typeThesiset

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