Abstract
Gestational Diabetes Mellitus (GDM) is one of the major diseases that affect pregnant women. On average, about 7% of pregnant women are affected by this disease. The consequences of non-treatment for the mother vary from the problems usually caused by Type 1 or 2 diabetes - such as dizziness, weight gain, hyperglycemia - to complications at the time of delivery. For the fetus, it can cause exaggerated weight gain, hypoglycemia, jaundice, type 2 diabetes, and even fetal death. Therefore, early diagnosis is important to indicate adequate follow-up and treatment in a timely manner. In this context, we carried out the structuring of the diseases that are manifested in concomitance or that are opportunized by the favorable environment caused by the evolution of undiagnosed Diabetes, through Bayesian Networks, with emphasis on Naive Bayes, based on data from a Health Plan Operator which covers eleven Brazilian states. Thus, the identification of these diseases and their respective symptoms can be used to support the early diagnosis of GDM.
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Filho, E.G., Pinheiro, P.R., Pinheiro, M.C.D., Nunes, L.C., Gomes, L.B.G., Farias, P.P.M. (2019). Support to Early Diagnosis of Gestational Diabetes Aided by Bayesian Networks. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_36
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DOI: https://doi.org/10.1007/978-3-030-19810-7_36
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