A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh

Abstract

Postpartum depression is a severe mental health issue exhibited among perinatal women after the childbirth process. While the negative impact of postpartum depression is extensive in developing countries, there is a significant lack of proper tools and techniques to predict the disorder due to negligence. This work proposes a machine learning-based system for finding the risk factors and prevalence of postpartum depression in Bangladesh. We developed a survey of different socio-demographic questions and modified questions from two standard postpartum depression screening scales (EPDS, PHQ-2). Data from 150 women have been collected, processed, and implemented in different machine learning models to find—the best performing models. Based on the collected data of the perinatal women in Bangladesh, the best performing machine learning model was Random Forest. The performance metrics for the best model were AUC: 98%, Accuracy: 89%, and Sensitivity: 89%. The performance of the models varies from 88%–98% (AUC), 82%–89% (Accuracy), and 81%–89% (Sensitivity). We have also found the top risk factors for causing PPD. According to this work, the prevalence of PPD in Bangladesh is 66.7% (Considering the medium and high chance of PPD). This proposed work is the first to detect the risk factors and prevalence of PPD in Bangladesh using a machine learning approach.