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Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of p...

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Autores principales: Qasrawi, Radwan, Amro, Malak, VicunaPolo, Stephanny, Abu Al-Halawa, Diala, Agha, Hazem, Abu Seir, Rania, Hoteit, Maha, Hoteit, Reem, Allehdan, Sabika, Behzad, Nouf, Bookari, Khlood, AlKhalaf, Majid, Al-Sabbah, Haleama, Badran, Eman, Tayyem, Reema
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445566/
https://www.ncbi.nlm.nih.gov/pubmed/36111217
http://dx.doi.org/10.12688/f1000research.110090.1
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author Qasrawi, Radwan
Amro, Malak
VicunaPolo, Stephanny
Abu Al-Halawa, Diala
Agha, Hazem
Abu Seir, Rania
Hoteit, Maha
Hoteit, Reem
Allehdan, Sabika
Behzad, Nouf
Bookari, Khlood
AlKhalaf, Majid
Al-Sabbah, Haleama
Badran, Eman
Tayyem, Reema
author_facet Qasrawi, Radwan
Amro, Malak
VicunaPolo, Stephanny
Abu Al-Halawa, Diala
Agha, Hazem
Abu Seir, Rania
Hoteit, Maha
Hoteit, Reem
Allehdan, Sabika
Behzad, Nouf
Bookari, Khlood
AlKhalaf, Majid
Al-Sabbah, Haleama
Badran, Eman
Tayyem, Reema
author_sort Qasrawi, Radwan
collection PubMed
description Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.
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spelling pubmed-94455662022-09-14 Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study Qasrawi, Radwan Amro, Malak VicunaPolo, Stephanny Abu Al-Halawa, Diala Agha, Hazem Abu Seir, Rania Hoteit, Maha Hoteit, Reem Allehdan, Sabika Behzad, Nouf Bookari, Khlood AlKhalaf, Majid Al-Sabbah, Haleama Badran, Eman Tayyem, Reema F1000Res Research Article Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries. F1000 Research Limited 2022-04-04 /pmc/articles/PMC9445566/ /pubmed/36111217 http://dx.doi.org/10.12688/f1000research.110090.1 Text en Copyright: © 2022 Qasrawi R et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Qasrawi, Radwan
Amro, Malak
VicunaPolo, Stephanny
Abu Al-Halawa, Diala
Agha, Hazem
Abu Seir, Rania
Hoteit, Maha
Hoteit, Reem
Allehdan, Sabika
Behzad, Nouf
Bookari, Khlood
AlKhalaf, Majid
Al-Sabbah, Haleama
Badran, Eman
Tayyem, Reema
Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title_full Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title_fullStr Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title_full_unstemmed Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title_short Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study
title_sort machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the covid-19 pandemic: a cross-sectional regional study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445566/
https://www.ncbi.nlm.nih.gov/pubmed/36111217
http://dx.doi.org/10.12688/f1000research.110090.1
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