Background: Globally, an estimated 13% of adolescents aged 10–19 are affected by mental disorders. As of 2020, the number of children under the age of 19 in Mongolia was reported to be 1,289,587, reflecting a 0.9% increase compared to 2015. A 2013 study on adolescents found that 60.5% were categorized as mentally healthy, 30.5% exhibited emotional and behavioral difficulties, and 9% were diagnosed with a mental disorder. A study conducted in Govi-Altai Province between 2018 and 2019 reported that 58.6% of adolescents were classified as healthy, 36.1% experienced psychological difficulties, and 5.2% were diagnosed with a mental disorder. Internationally, adolescent mental health has been widely assessed using the Strengths and Difficulties Questionnaire (SDQ), with an increasing number of studies employing artificial intelligence-based predictive models. However, in Mongolia, research utilizing artificial intelligence and machine learning for analyzing large-scale mental health data remains limited. This gap underscores the need for the present study. Aim: Machine learning models were compared to determine adolescent emotional and behavioral problems using the SDQ. Materials and Methods: Data was collected from teenagers, teachers, and parents in Govi-Altai Province, and the databases were created for each group. The teenager database was divided into 10 folds by cross-validation, and the models were developed using machine learning methods and evaluated using their performance measures. The results were mainly analyzed using the Bayes model. Results: The teenagers have emotional and behavioral problems due to emotional and peer interactions, but they are at risk of developing disorders due to hyperactivity and behavioral changes. Conclusion Comparing the model performance results with previous studies, Bayesian model accuracy decreased by 0.03, sensitivity decreased by 0.08, and specificity increased by 0.01. Also, the difference between the performance evaluation metrics of the C50 and Bayesian models is very small, between 0.01 and 0.02. This shows that the performance of the Bayesian method is good when the number of attributes in the database increases. Compared to the results of the knowledge generated by the research, the participants are more likely to develop emotional and behavioral disorders due to their peer relationship indicators, such as other children generally not liking them, getting on better with adults, and due to emotional symptoms such as being unhappy and depressed.

Introduction: Following the COVID-19 lockdown, the introduction of powered mopeds and scooters into urban mobility in Mongolia, without basic traffic regulatory frameworks such as vehicle classification, participation rules, age limits for riders, or parking regulations may have contributed to an increased risk of pedestrian injuries. Objective: To examine temporal trends in pedestrian injuries in Mongolia and to assess the impact of the emergence of micro mobility, while accounting for COVID-19 lockdowns, using an interrupted time-series analysis. Methods: We conducted an interrupted time-series analysis using national data on pedestrian injuries in Mongolia from January 2016 to December 2024. Monthly pedestrian injury rates per 10,000 population were calculated and stratified by age groups, regressing it on the number of months after January 2016 (the beginning of the study), after November 2020 (the lockdowns started), after May 2021 (the lockdown lifted and new mode of urban transportation started to release). Results: During the 9-year study period, there were 41,063 pedestrians, of whom 51.5% were male. The monthly rate of overall pedestrian injury started to decrease after the COVID-19 lockdowns possibly due to reduced traffic activity. However, following the lifting of restrictions, the trend started to increase among the age groups under 10 years (1.8%, 95% CI: 0.8–2.8), 10–19 years (1.6%, 95% CI: 0.3–2.9), 50–59 years (0.6%, 95% CI: 0.1–1.2), and 60–69 years (1.3%, 95% CI: 0.4–2.2). Conclusion The increase in pedestrian injuries suggests that the emergence of micromobility may have contributed to higher injury risk for pedestrians in Mongolia. This highlights the urgent need for change in infrastructure and regulations governing micromobility usage to enhance pedestrian safety.