Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2025 Jan 13;25(1):18.
doi: 10.1186/s12911-024-02848-x.

Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis

Meng Zhao  1 , Zhixin Yao  1 , Yan Zhang  1 , Lidan Ma  1 , Wenquan Pang  1 , Shuyin Ma  2 , Yijun Xu  3 , Lili Wei  4
Affiliations
Meta-Analysis

Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis

Meng Zhao et al. BMC Med Inform Decis Mak. .

Abstract

Background: This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).

Methods: A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science up to July 02, 2024. The quality of the studies included was assessed. The risk of bias was assessed through the prediction model risk of bias assessment tool and a graph was drawn accordingly. The meta-analysis was performed using Stata15.0.

Results: A total of 13 studies were included in the present review, involving 11,320 GDM patients and 22 ML models. The meta-analysis for ML models showed a pooled C-statistic of 0.82 (95% CI: 0.79 ~ 0.86), a pooled sensitivity of 0.76 (0.72 ~ 0.80), and a pooled specificity of 0.57 (0.50 ~ 0.65).

Conclusion: ML has favorable diagnostic accuracy for the progression of GDM to T2DM. This provides evidence for the development of predictive tools with broader applicability.

Keywords: Clinical prediction model; Gestational diabetes mellitus; Machine learning; Systematic review; Type 2 diabetes mellitus.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of study selection
Fig. 2
Fig. 2
Quality assessment results of ML models in included studies
Fig. 3
Fig. 3
Forest plot of C-statistic
Fig. 4
Fig. 4
Forest plot of sensitivity and specificity
See this image and copyright information in PMC

References

    1. Wang J, Zheng J, Shi W, Du N, Xu X, Zhang Y, et al. Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut. 2018;67(9):1614–25. 10.1136/gutjnl-2018-315988. - PMC - PubMed
    1. Saravanan P. Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 2020;8(9):793–800. 10.1016/s2213-8587(20)30161-3. - PubMed
    1. Ravaut M, Harish V, Sadeghi H, Leung KK, Volkovs M, Kornas K, et al. Development and validation of a Machine Learning Model Using Administrative Health Data to predict onset of type 2 diabetes. JAMA Netw Open. 2021;4(5):e2111315. 10.1001/jamanetworkopen.2021.11315. - PMC - PubMed
    1. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL. Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ. 2020;369:m1361. 10.1136/bmj.m1361. - PMC - PubMed
    1. Ratner RE, Christophi CA, Metzger BE, Dabelea D, Bennett PH, Pi-Sunyer X, et al. Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions. J Clin Endocrinol Metab. 2008;93(12):4774–9. 10.1210/jc.2008-0772. - PMC - PubMed

LinkOut - more resources