Machine Learning-Based Prediction of Abdominal Subcutaneous Fat Thickness During Pregnancy.

Objective

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.