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

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026
dc.authorscopusidRadwan Qasrawi / 57212263325en_US
dc.authorwosidRadwan Qasrawi / AAA-6245-2019
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorAmro, Malak
dc.contributor.authorVicunaPolo, Stephanny
dc.contributor.authorAbu Al-Halawa, Diala
dc.contributor.authorAgha, Hazem
dc.contributor.authorAbu Seir, Rania
dc.contributor.authorHoteit, Maha
dc.contributor.authorHoteit, Reem
dc.contributor.authorAllehdan, Sabika
dc.contributor.authorBehzad, Nouf
dc.contributor.authorBookari, Khlood
dc.contributor.authorAlKhalaf, Majid
dc.contributor.authorAl-Sabbah, Haleama
dc.contributor.authorBadran, Eman
dc.contributor.authorTayyem, Reema
dc.date.accessioned2022-11-04T05:57:10Z
dc.date.available2022-11-04T05:57:10Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground: 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.en_US
dc.identifier.citationQasrawi, R., Amro, M., VicunaPolo, S., Abu Al-Halawa, D., Agha, H., Abu Seir, R., . . . Tayyem, R. (2022). Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: A cross-sectional regional study. F1000Research, 11 doi:10.12688/f1000research.110090.1en_US
dc.identifier.doi10.12688/f1000research.110090.1en_US
dc.identifier.scopus2-s2.0-85137764727en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.12688/f1000research.110090.1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3220
dc.identifier.volume11en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherF1000 Research Ltden_US
dc.relation.ispartofF1000Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnxietyen_US
dc.subjectCOVID-19en_US
dc.subjectDepressionen_US
dc.subjectMachine Learningen_US
dc.subjectPregnancyen_US
dc.subjectRandom Foresten_US
dc.titleMachine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional studyen_US
dc.typeArticleen_US

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