Predicting Neonatal Hypoglycemia Using AI Neural Networks in Infants from Mothers with Gestational Diabetes Mellitus

Zhimin Zhang; Ying Feng; Yu Zhang; Xia Li; Yanfei Li; Lulu Sun; Xianying Li; Hui Du; Jingxiao Zhang

doi:10.12659/MSM.944513

11 October 2024 : Clinical Research

Predicting Neonatal Hypoglycemia Using AI Neural Networks in Infants from Mothers with Gestational Diabetes Mellitus

Zhimin Zhang^1ABCDEFG, Ying Feng^1ABCDEF, Yu Zhang^2ABC, Xia Li^1ABC, Yanfei Li^3BCF, Lulu Sun^1BC, Xianying Li^1D, Hui Du^1AEFG, Jingxiao Zhang^1AEFG*

DOI: 10.12659/MSM.944513

Med Sci Monit 2024; 30:e944513

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Table 2 Comparison of the performance of 4 models across datasets in predicting the lowest neonate blood glucose concentrations in the first 24 h of life.

Model	R2 score	MSE	RMSE
ANNs	0.869	0.075	0.274
Multiple linear regression	0.667	0.193	0.437
Random forest	0.810	0.109	0.330
SVR	0.714	0.164	0.405
ANNs – Artificial Neural Networks; SVR – support vector regression; R Score – coefficient of determination; MSE – mean square error; RMSE – root mean square error.

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Predicting Neonatal Hypoglycemia Using AI Neural Networks in Infants from Mothers with Gestational Diabetes Mellitus

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