JMIR Publications

ABSTRACT

Background:

Postpartum depression (PPD) is a serious public health problem. Building a predictive model of PPD using data in pregnancy can facilitate earlier identification and intervention.

Objective:

To compare the effects of four different machine learning models using data in pregnancy to predict PPD and explore which factors in the model are the most important for PPD prediction.

Methods:

Information on the pregnancy period from a cohort of 508 women, including demography, social environmental factors and mental health, was used as predictors in the models. Edinburgh Postnatal Depression Scale (EPDS) score within 42 days after delivery was used as the outcome indicator. Using two feature selection methods (expert consultation and filter feature selection based on random forest (FFS-RF)) and two algorithms (support vector machine (SVM) and random forest (RF)), we developed four different machine learning PPD prediction models and compared their prediction effects.

Results:

There was no significant difference in the effectiveness of the two feature selection methods in terms of model prediction performance, but 10 fewer factors were selected with the FFS-RF than with the expert consultation method. The model based on SVM and FFS-RF had the best prediction effects (sensitivity = 0.69, AUC = 0.78). In the feature importance ranking output by the RF algorithm, psychological elasticity, depression during the third trimester and income level were the most important predictors.

Conclusions:

In contrast to the expert consultation method, FFS-RF was important in dimension reduction. When the sample size is small, the SVM algorithm is suitable for predicting PPD. In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.