Predicting the risk of preterm birth with machine learning and electronic health records in China

Abstract

Background: Preterm birth is a serious global public health issue, and early prediction in pregnant women is crucial for timely intervention and reduction of the incidence preterm births. We aimed to predict and validate the risk of preterm birth with machine learning, deep learning, and electronic health records in China. Materials and methods: Data were collected from 58,424 pregnant women between May 2015 and April 2024. After excluding incomplete records, a total of 36,378 cases were included, consisting of 34,132 full-term births and 2,246 preterm births. Of the 24 known high-risk factors for preterm birth, 20 statistically significant features were identified for model construction. Six machine learning algorithms were applied to process the data containing missing values, and 22 models were developed for predicting preterm births using the imputed data. Additionally, two dynamic deep learning methods were incorporated in our model development process. Results: Among the machine learning models, the Random Forest model performed best in both datasets with missing values and imputed data, achieving a maximum AUC of 0.826. The LightGBM model also exhibited strong performance, even with fewer features. Among the deep learning models, the LSTM model performed better, with an AUC of 0.851. Additionally, data from 10,367 pregnant women, collected between May and December 2024, were used as an external validation set, confirming the model’s stability. Conclusions: The findings of this study indicate that both machine learning and deep learning models using electronic health records are valuable for preterm birth risk screening, supporting their use in clinical practice for preterm birth risk management. Clinical trial number: Not applicable.