Gestatsioondiabeedi ja makrosoomia prognoosimine ning nende riskitegurite analüüs masinõppe meetoditega
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Large-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.
Description
Keywords
large for gestational age, macrosomia, gestational diabetes mellitus, machine learning, data mining, binary classification, feature selection CERCS: P160 Statistics, operation research, programming, actuarial mathematics, B570