Postpartum Depression Detection Using Machine Learning and EPDS Data

Author(s)	Ms. Pradeepthi Diyyala, Ms. Divya Dharmalingam, Ms. KavyaShri K, Ms. Vismaya A, Ms. Shalini Kumari Kumari
Country	India
Abstract	Many new moms suffer from postpartum depression (PPD), a serious mental disorder that often results in long-lasting emotional and psychological suffering. Effective intervention depends on early identification; however, existing screening techniques, such as the Edinburgh Postnatal Depression Scale (EPDS), rely on subjective self-reports, which can lead to a diagnosis that is inconsistent and time-consuming. This work explores the utilization of machine learning (ML) to enhance the precision and effectiveness of PPD screening and overcome these constraints. An EPDS dataset was utilized to create and assess a variety of machine learning models, including Random Forest, XG Boost, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). The MLP model outperformed conventional ML techniques like XG Boost (92%), with a maximum accuracy of 96%, followed closely by LSTM (94%).
Keywords	Postpartum depression, EPDS, Random Forest, XG Boost, Support Vector Machine, Multi Layer Perceptron, Long Short Term Memory.
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.41183
Short DOI	https://doi.org/g9fcbj

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International Journal For Multidisciplinary Research