Early Prediction of High Risk Gestational Diabetes Mellitus <i>via</i> Machine Learning Models

Abstract

Abstract Aims: Gestational diabetes mellitus (GDM) is a pregnancy-specific disorder that can usually be diagnosed after 24 gestational weeks. So far, there is no accurate method to predict GDM in early pregnancy. Methods: We collected data extracted from the hospital’s electronic medical record system included 73 features in the first trimester. We also recorded the occurrence of GDM, diagnosed at 24-28 weeks of pregnancy. We conducted a feature selection method to select a panel of most discriminative features. We then developed advanced machine learning models, using Deep Neural Network (DNN), Support Vector Machine (SVM), K-Nearest Neighboring (KNN), and Logistic Regression (LR), based on these features. Results: We studied 16,819 women (2,696 GDM) and 14,992 women (1,837 GDM) for the training and validation group. DNN, SVM, KNN, and LR models based on the 73-feature set demonstrated the best discriminative power with corresponding area under the curve (AUC) values of 0.92 (95%CI 0.91, 0.93), 0.82 (95%CI 0.81, 0.83), 0.63 (95%CI 0.62, 0.64), and 0.85 (95%CI 0.84, 0.85), respectively. The 7-feature (selected from the 73-feature set) DNN, SVM, KNN, and LR models had the best discriminative power with corresponding AUCs of 0.84 (95%CI 0.83, 0.84), 0.69 (95%CI 0.68, 0.70), 0.68 (95%CI 0.67, 0.69), and 0.84 (95% CI 0.83, 0.85), respectively. The 7-feature LR model had the best Hosmer-Lemeshow test outcome. Notably, the AUCs of the existing prediction models did not exceed 0.75. Conclusions: Our feature selection and machine learning models showed superior predictive power in early GDM detection than previous methods; these improved models will better serve clinical practices in preventing GDM. Funding Statement: This study was supported by the National Key Research and Development Program of China (2018YFC1002804, 2016YFC1000203), the National Natural Science Foundation of China (81671412, 81661128010), Foundation of Shanghai Municipal Commission of Health and Family Planning (20144Y0110), and Clinical Skills Improvement Foundation of Shanghai Jiaotong University School of Medicine (JQ201717). Declaration of Interests: No potential conflicts of interest relevant to this article were reported. Ethics Approval Statement: This study was approved by the Institutional Review Board of the International Peace Maternity and Child Health Hospital.