JMIR Publications

ABSTRACT

Background:

Background:

Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.

Objective:

Objective:

This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models achieve precise prediction of PPD in the Chinese population, and interpret model to reveal clinical implications.

Methods:

Methods:

This study recruited pregnant women who delivered at the West China Second University Hospital, Sichuan University. Various variables were collected from electronic medical record data and screened using least absolute shrinkage and selection operator penalty (LASSO) regression. Participants were divided into training (70%) and validation (30%) sets by random sampling. Machine learning-based predictive models were developed in the training cohort. Models were validated in the validation cohort with receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Multiple model interpretation methods were implemented to explain the optimal model.

Results:

Results:

We recruited 2,055 participants in this study. The extreme gradient boosting model was the optimal predictive model with the area under the receiving operating characteristic (AUC) of 0.850. SHapley Additive exPlanation indicated the most influential predictors of PPD were antepartum depression, elevated thyroid-stimulating hormone, declined thyroid peroxidase antibodies, elevated serum ferritin, older age, and lower fetal weight.

Conclusions:

Conclusion: This study developed and validated a machine learning based predictive model for PPD. Several significant risk factors and how they impact the prediction of PPD were revealed. These findings provide new insights into the early screening of high-risk individuals for PPD, emphasizing the need for comprehensive screening approaches that include both physiological and psychological factors.