Predictive Analysis of Postpartum Depression Using Machine Learning

Area of Science:

• Psychiatry
• Maternal Health
• Machine Learning in Healthcare

Background:

• Postpartum depression (PPD) is a significant mental health challenge impacting mothers and families.
• Identifying predictors and developing early detection models for PPD is crucial for timely intervention.
• Existing research highlights various risk factors, but predictive modeling offers enhanced insights.

Purpose of the Study:

• To investigate factors influencing maternal postpartum depression.
• To develop and evaluate a machine learning-based predictive model for PPD risk.
• To identify key predictors for early identification and intervention strategies.

Main Methods:

• Utilized the Korean Early Childhood Education and Care Panel (K-ECEC-P) dataset (n=2570).
• Applied machine learning classifiers including logistic regression, decision trees, random forest, and AdaBoost.
• Evaluated model performance using precision, accuracy, recall, F1-score, and AUC.

Main Results:

• Logistic regression model demonstrated superior performance in predicting PPD.
• Significant predictors identified: conflict with a partner, stress, and value of children.
• Increased partner conflict and stress were associated with higher PPD likelihood; higher value of children reduced risk.

Conclusions:

• Partner conflict and stress are potent predictors of maternal postpartum depression.
• A positive valuation of children acts as a protective factor against PPD.
• Maternal psychological well-being and environmental factors require careful management postpartum.