Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study

• Published in: JMIR medical informatics Vol. 13; p. e58649
• Main Authors: Zhang, Ren, Liu, Yi, Zhang, Zhiwei, Luo, Rui, Lv, Bin
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 20.01.2025
• Subjects: Adaptation; Adult; Algorithms; Applications of AI; China - epidemiology; Clinical Informatics; Clinical Mental Health Informatics; Decision trees; Depression, Postpartum - diagnosis; Development and Evaluation of Research Methods, Instruments and Tools; Diagnostic Tools in Mental Health; Families & family life; Female; Humans; Machine Learning; Mental depression; Mental disorders; Methods and New Tools in Mental Health Research; Original Paper; Perinatal Depression; Postpartum Depression; PPD; Personal health; Postpartum depression; Pregnancy; Retrospective Studies; Risk Factors; ROC Curve; Social support; Variables; China; risk factors; predictive model; PPD; XGBoost; extreme gradient boosting; postpartum depression; machine learning
• DOI: 10.2196/58649
• ISSN: 2291-9694

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. This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications. 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 regression. Participants were divided into training (1358/2055, 66.1%) and validation (697/2055, 33.9%) 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 curve and decision curve analysis. Multiple model interpretation methods were implemented to explain the optimal model. We recruited 2055 participants in this study. The extreme gradient boosting model was the optimal predictive model with the area under the receiver operating curve of 0.849. Shapley Additive Explanation indicated that the most influential predictors of PPD were antepartum depression, lower fetal weight, elevated thyroid-stimulating hormone, declined thyroid peroxidase antibodies, elevated serum ferritin, and older age. 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 individuals with high risk for PPD, emphasizing the need for comprehensive screening approaches that include both physiological and psychological factors.