Predicting Postpartum Depression in Maternal Health Using Machine Learning

Maria Alejandra Terreros-Lozano, Diana Lopez-Soto, Samuel Nucamendi-Guillén, María Alejandra López-Ceballos

2025

Abstract

Postpartum depression (PPD) is a severe mental health condition affecting mothers after childbirth, characterized by prolonged sadness, anxiety, and fatigue. Unlike the transient "baby blues," PPD's symptoms can last months, impacting a mother's ability to care for herself and her baby. In the U.S., PPD affects about 1 in 7 women, with a significant rise in prevalence from 13.8% to 19.8% in recent years. This condition leads to adverse effects on maternal and infant health. Early diagnosis and treatment of PPD can help prevent longterm depression and minimize the emotional and financial burden associated with the condition. This research aims to evaluate machine learning models to predict PPD risk. Critical factors were identified, and an accuracy of 96.57% and a precision of 99.88% were obtained. This predictive model enables early, personalized interventions, aiming to improve maternal health outcomes and reduce the societal burden of PPD.

Paper Citation

in Harvard Style

Terreros-Lozano M., Lopez-Soto D., Nucamendi-Guillén S. and López-Ceballos M. (2025). Predicting Postpartum Depression in Maternal Health Using Machine Learning. In Proceedings of the 14th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-732-0, SciTePress, pages 255-263. DOI: 10.5220/0013155700003893

in Bibtex Style

@conference{icores25,
author={Maria Terreros-Lozano and Diana Lopez-Soto and Samuel Nucamendi-Guillén and María López-Ceballos},
title={Predicting Postpartum Depression in Maternal Health Using Machine Learning},
booktitle={Proceedings of the 14th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2025},
pages={255-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013155700003893},
isbn={978-989-758-732-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Predicting Postpartum Depression in Maternal Health Using Machine Learning
SN - 978-989-758-732-0
AU - Terreros-Lozano M.
AU - Lopez-Soto D.
AU - Nucamendi-Guillén S.
AU - López-Ceballos M.
PY - 2025
SP - 255
EP - 263
DO - 10.5220/0013155700003893
PB - SciTePress