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

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.

Prevalence of Perinatal Anxiety and Related Disorders in Low- and Middle-Income Countries

“…Generalized anxiety disorder (including anxiety not specified) was the most commonly reported anxiety disorder, examined by 184 of 203 studies (90.6%) . The pooled point prevalence for generalized anxiety disorder was 22.2% (95% CI, 19.4%-25.0%; n = 173 553).…”

“…Our search identified 10 617 studies; 1231 full texts were reviewed, and of these, 203 studies were included (Figure 1; eTable 2 in Supplement 1). Cumulatively, outcomes were reported for 212 318 perinatal women (from conception to 12 months post birth) from 33 LMICs. The prevalence estimates of 6 anxiety and related disorders were reported: generalized anxiety disorder, social anxiety disorder, posttraumatic stress disorder, obsessive-compulsive disorder, panic disorder, and adjustment disorder.…”

“…The prevalence was significantly lower post partum (16.2%; 95% CI, 12.5%-20.3%; 32 studies; n = 17 960; P = .02). Nine studies measured the prevalence of pregnant and postpartum women, with combined results showing a prevalence of 16.4% (95% CI, 5.9%-30.6%; n = 6108).…”

“…The prevalence was significantly lower post partum (16.2%; 95% CI, 12.5%-20.3%; 32 studies 30,32,34,38,54,65,68,69,80,86,90,93,100,107,118,120,122,130,133,139,153,155,168,170,175,176,182,183,186,199,227,229 ; n = 17 960; P = .02). Nine studies 60,102,105,128,134,143,162,169,171 measured the prevalence of pregnant and postpartum women, with combined results showing a prevalence of 16.4% (95% CI, 5.9%-30.6%; n = 6108).…”

Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality

“…According to [55][56][57], the AHP method can be summarized as the breakdown of a complex, unstructured situation into its parts. These parts, or variables, are arranged in hierarchical order; numerical values and subjective judgments are designated, denoting each variable's relative importance.…”