Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms.

This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original).

Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea.

This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth.