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

Authors: Md. Zahurul Haque; Tasnim Binta Anowar; Sumaiya Jannat Samira

Addresses: Department of CSE, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh ' Department of CSE, Manarat International University, Gulshan-2, Dhaka 1212, Bangladesh ' Department of CSE, Manarat International University, Gulshan-2, Dhaka 1212, Bangladesh

Abstract: Antenatal and postpartum depression (APD) are significant maternal mental health concerns, especially in low-resource 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 resource-limited environments.

Keywords: antenatal depression; postpartum depression; machine learning; mental health prediction; AI in healthcare.

DOI: 10.1504/IJCAST.2026.155956

International Journal of Complexity in Applied Science and Technology, 2026 Vol.2 No.3, pp.302 - 316

Received: 30 Apr 2025
Accepted: 14 Jul 2025
Published online: 27 Aug 2026 *

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