AI-driven prediction model for antenatal and postpartum depression among Bangladeshi pregnant mothers

Abstract Antenatal and postpartum depression (APD) are significant maternal mental health concerns, especially in lowresource settings like Bangladesh. This study introduces an AI-driven prediction model aimed at identifying the risk of APD among Bangladeshi mothers. Data were gathered from over 500 participants via Google Forms distributed through hospitals, online platforms, and community networks. The dataset encompasses a range of demographic, psychological, and lifestyle factors. Machine learning algorithms—Random Forest, XGBoost, and Gradient Boosting—were employed, demonstrating high accuracy in predicting depression severity. The developed web-based application enables real-time risk assessments, facilitating early detection and timely intervention. This research highlights the transformative role of AI in enhancing maternal mental health services and delivering scalable, data-driven solutions in resourcelimited environments.