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.

No abstract

No abstract

Serious games (SGs) are interactive and entertaining digital games with a special educational purpose. Studies have shown that SGs are effective in enhancing educational skills. Cognitive skills training through serious games have been used in improving students learning outcomes. In this article, we introduce the 'plants kingdom' serious game for improving adolescents' cognitive skills, mainly attention (Focus, selection, and sustained attention) and understanding skills. The game used the grade 8 Science book in designing the game content. The plant kingdom lesson was used for developing the game story and objects, its methods and tools were designed for the purpose of attention and understanding skills improvement. The game was evaluated on 43 students from public schools between the ages of 13-15 years, the study selected data from the students who had completed 5 playing sessions. The attention and understanding skills were assessed using the automatic recording and analysis of the game player's data. The variables utilized from the players' data included player ID, session number, gender, number of trials, level, drag and drop time, distance, reason for failure, position, speed, status, time, and playing tool. Results showed that the game improved the attention and understanding skills of students by 27% and 25 % respectively. The study showed the significant effect of serious games in enhancing students' cognition; thus, integrating serious games into the education system can potentially improve learning objectives and outcomes.