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

Kurt B., Gürlek B., Keskin S., Özdemir S., Karadeniz Ö., Kırkbir İ., ...More

Medical & biological engineering & computing, vol.61, no.7, pp.1649-1660, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 7
  • Publication Date: 2023
  • Doi Number: 10.1007/s11517-023-02800-7
  • Journal Name: Medical & biological engineering & computing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Page Numbers: pp.1649-1660
  • Keywords: Gestational diabetes (GD), Clinical decision support system, Deep learning, Bayesian optimization, SVM, Random forest
  • Karadeniz Technical University Affiliated: Yes


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.