%0 Journal Article
%T Machine Learning-Based Prediction of Abdominal Subcutaneous Fat Thickness During Pregnancy.
%A Hwang MS
%A Song E
%A Ahn J
%A Park S
%J Metab Syndr Relat Disord
%V 21
%N 9
%D 2023 Nov 5
%M 37669001
%F 2.363
%R 10.1089/met.2023.0043
%X 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.