Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study [version 1; peer review: 2 approved]

Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study [version 1; peer review: 2 approved]

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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Bibliographic Details
Main Authors: Maha Hoteit, Eman Badran, Reema Tayyem, Nouf Behzad, Haleama Al-Sabbah, Reem Hoteit, Rania Abu Seir, Khlood Bookari, Sabika Allehdan, Stephanny VicunaPolo, Majid AlKhalaf, Malak Amro, Hazem Agha, Diala Abu Al-Halawa, Radwan Qasrawi
Format: Article
Language:English
Published: F1000 Research Ltd 2022-04-01
Series:F1000Research
Subjects:
Machine Learning
Anxiety
Depression
Pregnancy
COVID-19
Random Forest
eng
Online Access:https://f1000research.com/articles/11-390/v1

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