Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE). Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation. Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80-0.92), the accuracy was 0.74 (95% CI 0.74-0.75), the precision was 0.82 (95% CI 0.79-0.84), the recall rate was 0.42 (95% CI 0.41-0.44), and Brier score was 0.17 (95% CI 0.17-0.17). Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.
Keywords: deep neural network; machine learning; prediction; preeclampsia; pregnancy.
Copyright © 2022 Liu, Yang, Chen, Ding, Shi, Sun, Huang, Liu, Liu, Yan and Li.