Abstract

Postpartum depression (PPD) is a serious and under-diagnosed mental health condition affecting mothers after the birth of a child, with severe consequences for the well-being of the mother and the infant. Early identification is difficult because it is multifactorial, reported subjectively, and the interactions between biological, psychological, and social factors are complex. This research suggests an intelligent and reliable machine learning framework for early prediction of PPD using multidimensional maternal health data. The dataset contains the demographic, clinical, psychosocial, and psychological attributes obtained through structured bilingual questionnaires and Edinburgh Postnatal Depression Scale (EPDS) assessments for 64 postpartum women with 28 mixed-type features. Advanced preprocessing, feature selection, and class balancing using SMote were exploited to resolve the problem of data heterogeneity and imbalance. The proposed hybrid deep learning model combines Convolutional Neural Networks (CNN) for feature extraction at different levels, Bidirectional Long Short-Term Memory (Bi-LSTM) for modeling in time, and an attention mechanism to pay Attention to clinically relevant indicators. Model evaluation was done using a 10-fold CV based on accuracy, sensitivity, specificity, and ROC-AUC metrics. The results show the proposed model is superior to conventional approaches as it has 96.8% prediction accuracy, 96.1% sensitivity, 97.4% specificity, and ROC-AUC 0.98. Ablation analysis validates the role of each component (CNN, Bi-LSTM, Attention, feature fusion, and SMote), which has a significant contribution to the prediction performance. The study further includes explainable AI tools (SHAP and LIME) for improved interpretability for clinical applications. These results suggest that artificial intelligence-enhanced prediction of PPD can identify high-risk mothers early, facilitate early psychological interventions, and improve maternal mental health outcomes. The proposed framework provides a scalable and clinically relevant solution to supporting evidence-based maternal healthcare decision-making and could reduce undiagnosed PPD in a real-world setting.