Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques-Reference-Cited by-同舟云学术

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

Published:2023-02-27 Issue:7 Volume:61 Page:1649-1660
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Kurt Burçin^ORCID,Gürlek Beril,Keskin Seda,Özdemir Sinem,Karadeniz Özlem,Kırkbir İlknur Buçan,Kurt Tuğba,Ünsal Serbülent,Kart Cavit,Baki Neslihan,Turhan Kemal

Funder

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-023-02800-7.pdf

Reference31 articles.

1. American Diabetes Association (2018) 2 Classification and diagnosis of diabetes. Diabetes Care 38(1):8-S6. https://doi.org/10.2337/dc15-S005

2. Türkiye Endokrinoloji ve Metabolizma Derneği. Diabetes Mellitus ve Komplikasyonlarının Tanı,Tedavi ve İzlem Kılavuzu (2017) Ankara, Miki Matbaacılık San. ve Tic. Ltd. Şti.

3. Farrar D, Duley L, Dowswell T, Lawlor DA (2017) Different strategies for diagnosing gestational diabetes to improve maternal and infant health. Cochrane Database Syst Rev 8(8):CD007122. https://doi.org/10.1002/14651858.CD007122.pub4

4. Vandorsten JP, Dodson WC, Espeland MA, Grobman WA, Guise JM, Mercer BM (2013) NIH consensus development conference: diagnosing gestational diabetes mellitus. NIH Consens State Sci Statements 29(1):1–31

5. DeSisto CL, Kim SY, Sharma AJ (2014) Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS) 2007–2010. Prev Chronic Dis 11:1–9. https://doi.org/10.5888/pcd11.130415

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