Development and application of a machine learning-based antenatal depression prediction model

Background: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and universally applicable prediction methods for AND in clinical practice. We leveraged sociodemographic and pregnancy-related data to develop and validate a machine learning-based AND prediction model. Methods: Data from 20,950 pregnant women form 3 hospitals were used and divided into training and test sets. AND was defined as an EPDS score of 10 or above. Using machine learning, we selected 34 characteristic variables and divided them into three categories based on clinical practice: Base Variables, General Variables, and Obstetric Variables. Based on this classification, we constructed four different AND random forest prediction models: the Base Model, the Base+General Model, the Base+Obstetric Model, and the Full Model. Results: The AUC range in the test set was 0.687-0.710. The Base+General Model achieved the best performance with an AUC of 0.710 (95 % CI: 0.693-0.710) in predicting AND risk during the late pregnancy period. The AUC of the Base Model was only 0.022 lower than that of the top-performing model, indicating its solid foundation for early AND screening. Limitations: We have only analyzed the dataset from two eastern cities, and have not yet validated our models in an external dataset. Conclusions: Machine learning-based prediction models offer the capability to anticipate the risk of AND across different pregnancy stages. This enables the earlier and more accurate identification of pregnant women who may be at risk, facilitating timely interventions for improving outcomes for both mothers and their offspring.