Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization.

A prospective study for gestational diabetes mellitus: Analysis of risk factors in Turkish women for early prediction.

Objective: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM.

Materials And Methods: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method.