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

Med Biol Eng Comput. 2023 Jul;61(7):1649-1660. doi: 10.1007/s11517-023-02800-7. Epub 2023 Feb 27.


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. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.

Keywords: Bayesian optimization; Clinical decision support system; Deep learning; Gestational diabetes (GD); Random forest; SVM.

MeSH terms

  • Bayes Theorem
  • Deep Learning*
  • Diabetes, Gestational* / diagnosis
  • Female
  • Humans
  • Machine Learning
  • Pregnancy
  • Prospective Studies