Postpartum Depression Risk Prediction using Hybrid Feature Selection and Ensemble Learning

Postpartum depression (PPD) is a common mental health disorder that occurs in women following childbirth, which often results in negative maternal and infant outcomes. Early detection is still a challenge because of the complex interaction of demographical, clinical and psychological factors. This research purpose is to develop a powerful machine learning framework, to accurately predict PPD risk. We used a combination of Chi-square, mutual information and Random Forest importance to select the most informative 25 features out of an initial set of 60 variables using a hybrid feature selection. Multiple machine learning models were trained such as Logistic Regression, Random Forest and Multi-Layer Perceptron (MLP) and their ensemble was implemented for better predictive performance. The proposed ensemble performed better with an accuracy of 91.3 %, F1-score of 90.4 %, and an AUC of 0.94, which is better than individual models and numerous state-of-the-art methods. These findings show that the hybrid feature selection combined with ensemble learning can offer a reliable and interpretable tool for the detection of early postpartum depression that can lead to an earlier intervention process and contribute to better maternal mental health outcomes.