An Integrated Context-Aware Adaptive Meta-Learning System for Accurate Risk Estimation of Postpartum Depression

PPD is a state of mood, behavior, or emotional disturbances caused by the interaction of biological, psychological, social, and behavioral factors that a woman experiences after childbirth. It is still a global public health issue. The Context-Aware Adaptive Meta-Learner (CAML) framework is a cutting-edge prediction model that uses multi-modal data and context detection to properly predict PPD risk. We collected 1,503 data on maternal health, network connections, and activity rhythms from a medical institution using e-survey questionnaires and wearable sensors. LSTM networks for temporal analysis, XGBoost for ranking accuracy, CNN for visual pattern identification, and Gradient Boosting Machines make up the CAML framework. Contextual modules teach social and family activity identification, and an Explainable Meta-Learner based on SHAP and LIME offers PPD risk variables. After k-fold cross-validation, transfer learning, and domain adaptation, this research had 98.7% predicted accuracy. It outperformed state-of-the-art approaches in CAML model predictability and interpretability. According to the study, multi-modal data integration, explainable AI, and context-wise analysis may change maternity care. The CAML framework gives healthcare practitioners clinical and assignable information to customize PPD prevention, mental health therapies, and ALM living experiences to new mothers' fundamentally vulnerable and heterogeneous social and clinical backgrounds.