Decision tree learning for predicting chronic postpartum depression in the Japan Environment and Children's Study

Background: Many studies have used machine learning techniques to construct predictive models of postpartum depression, but few such models are simple enough to use in community maternal health settings with pen and paper. Here, we use a decision tree to construct a prediction model for chronic postpartum depression.

Methods: Participants were 84,091 mothers. Chronic postpartum depression was identified as an Edinburgh Postnatal Depression Scale score of ≥9 at both 1 and 6 months postpartum. The training dataset included 84 diverse variables measured during pregnancy, including health status and biomarkers. In learning, the branching depth was constrained to 3, the number of branches per branch to 4, and the minimum number of n in a branch was 100. The training to validation data ratio was set to 7:3.

Results: A decision tree with 35 branches and an area under the receiver operating characteristic of 0.84 was created. Ten of 84 variables were extracted, and the most effective in classification was "feeling worthless." At training (n = 58,635), the most and least prevalent branches were 73.2 % and 0.84 % (mean = 6.29 %), respectively; at validation (n = 25,456), they were 60.4 % and 0.72 % (mean = 6.52 %), respectively.

Limitations: Chronic postpartum depression was identified using self-administered questionnaires.

Conclusions: This study created a simple and relatively high-performing prediction model. Because the model can be easily understood and used without expertise in machine learning, it is expected to be useful in maternal health settings, including grassroots community health.