Hepatitis B (HBV) and C (HCV) are viral infections which can cause acute and chronic hepatitis and are the leading causes for hepatic cirrhosis and cancer, thus creating a significant burden to healthcare systems due to the high morbidity/mortality and costs of treatment. The risk of HBV infection in an unvaccinated person from a single HBV-infected needle stick injury ranges from 6–30. The prevention of HBV infection among HCWs has become a crucial issue. HBV can effectively be prevented by vaccination. A safe and effective HBV vaccine has been available since the 1980s and can prevent acute and chronic infection with an estimated effectivity of 95%. In 2017, the São Paulo Declaration on Hepatitis was launched at the World Hepatitis Summit 2017, calling upon governments to include hepatitis B vaccines for HCWs in national immunization programs. The vaccine is 95% effective in preventing infection and its chronic consequences and has an outstanding record of safety and effectiveness. Data on current hepatitis B vaccine coverage among HCWs in Mongolia is scarce. According to Azzaya et al, the protection level of the subjects was 67.2% >100 mIU/ml, 18.8%, 11-100 mIU/mL and 14.1%, 0-10 mIU/mL based on antibody titer level respectively among the vaccinated HCWs at the 2nd Central hospital. Thus, the HBV vaccination among public and private sector HCWs in Mongolia to inform the health authorities about the HCWs HBV vaccination status along with associated problems and challenges for further improving vaccination strategy among HCWs.

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.