Current evidence regarding the safety of abdominal subcutaneous injections in pregnant women is limited. In this study, we developed a predictive model for abdominal skin–subcutaneous fat thickness (S-ScFT) by gestational periods (GP) in pregnant women.
Methods:
A total of 354 cases were measured for S-ScFT. Three machine learning algorithms, namely deep learning, random forest, and support vector machine, were used for S-ScFT predictive modeling and factor analysis for each abdominal site. Data analysis was performed using SPSS and RapidMiner softwares.
Results:
The deep learning algorithm best predicted the abdominal S-ScFT. The common important variables in all three algorithms for the prediction of abdominal S-ScFT were menarcheal age, prepregnancy weight, prepregnancy body mass index (categorized), large fetus for gestational age, and alcohol consumption.
Conclusion:
Predicting the safety of subcutaneous injections during pregnancy could be beneficial for managing gestational diabetes mellitus in pregnant women.
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