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Qasrawi R, Amro M, VicunaPolo S, Abu Al-Halawa D, Agha H, Abu Seir R, Hoteit M, Hoteit R, Allehdan S, Behzad N, Bookari K, AlKhalaf M, Al-Sabbah H, Badran E, Tayyem R. Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study. F1000Res 2022; 11:390. [PMID: 36111217 PMCID: PMC9445566 DOI: 10.12688/f1000research.110090.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 02/02/2023] Open
Abstract
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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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
- Dpertment of Computer Engineering, Istinye University, Istanbul, 34010, Turkey
| | - Malak Amro
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Stephanny VicunaPolo
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Diala Abu Al-Halawa
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Hazem Agha
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Rania Abu Seir
- Department of Medical Laboratory Sciences, Al-Quds University, Jerusalem, Palestinian Territory
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
- PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Lebanese University Nutrition Surveillance Center (LUNSC), Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut, Lebanon
| | - Reem Hoteit
- Clinical Research Institute, American University of Beirut, Bliss Street, Riad El Solh 1107 2020, Beirut, Lebanon
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Nouf Behzad
- Salmaniya Medical Complex, Ministry of Health, Manama, Bahrain
| | - Khlood Bookari
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Medna, Saudi Arabia
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Majid AlKhalaf
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Haleama Al-Sabbah
- Department of Health Sciences, Zayed University, Dubai, United Arab Emirates
| | - Eman Badran
- Faculty of Medicine, University of Jordan, Amman, Jordan
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha, Qatar
- Department of Nutrition and Food Technology, Faculty of Agriculture, The University of Jordan, Amman, 11942, Jordan
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