Table 1 Previously published machine learning-based GDM risk prediction models.

Qiu et al.¹⁷

4,378 women

Cost-sensitive hybrid model of logistic regression, support vector machine and CHAID tree

0.998

0.622

0.847

Zheng et al.¹⁸

4,771 women

Multivariate Bayesian logistic regression

0.75

0.66

0.766

Ye et al.¹⁹

22,242 pregnancies

Gradient boosting decision tree

0.99

0.15

0.74

0.26

0.90

Artzi et al.²⁰

588,622 pregnancies

Gradient boosting

–

–

0.854

Xiong et al.²¹

490 women

Light gradient boosting machine

0.995

0.883

0.942

Yan et al.²²

3,988 women

Logistic regression

–

0.706

0.779

Hou et al.²³

1,000 samples

Light gradient boosting machine

–

–

0.852

Wu et al.²⁴

32,190 women

Deep neural network

0.82

0.63

0.80

Wu et al.²⁵

17,005 women

Random forest

0.269

0.91

0.746

0.524

0.75

0.777

0.487