The main mechanism of sand sauna therapy is lie into heated sand baths for adults 8 to 20 min and for child 7 to 15 min long, and it is performed twice a day (morning and afternoon) for 2 or 3 weeks once a year. Particularly, the sanatorium offers a type of balneotherapy through the application of thermally warmed sand and a combination of healthy micro-bioclimate, purity of the natural environment, and the presence of fresh and healthy local foods to treat a wide range of medical conditions. The sand sauna treatment most likely affect the body by means of the sand’s high temperature and the presence of chemical elements those are biologically important. One of the well-experienced health resorts is the Tavan Els kidney sanatorium. The Tavan Els sanatorium treats patients with the following signs: fatigue, shortness of breath, bone and back pain, swelling of the legs, high blood pressure (over 130/80mmHg), infrequent urination, proteinuria, and blood in the urine. Their goal is to delay progression of chronic kidney diseases prevent or slow additional damage to the patient’s kidneys, improve blood circulation, decrease the use of medicines, and educate patients about alternative therapies. Our study investigated effects of sand sauna treatment more considering some laboratory findings. There conducted totally 720 patients where 429 inpatients and 291 outpatients. This study selected 150 patients with chronic glomerulonephritis symptoms of swelling 31%, blood in urine 9%, high blood pressure (Over 130/80 mmHg) 10% and mixed conditions 50%. After the 10 days treatment, the proteinuria was decreased 52.6%, and blood in urine decreased 16, 6% from the total number of patients. Hypertension was decreased 35% of patients from 10 to 20mmHg and 45% of patients did not changed. A swelling was cleared 51%, 38% decreased and 3% did not change. 66% of patients down the weight loss about 2-4kg, and 34% of patients did not change. We observed typical changes during 15-20 minutes bathing, heart rate increased from 65±1.3 bpm at rest to 82±2.4 bpm in 45°C hot sand bath. Blood pressure increased from 132±4.0/82±2.2 mmHg at rest to 139±1.4/81±1.0 at the bathing. Body temperature was increased 1- 2°C during bathing. Because of the equipment limits, we could not check the urinary sediment, and serum creatinine. Sand sauna treatment appears to be an effective and useful method to treat financial and emotional burdens problems of patients with chronic kidney disease problems that especially for the symptoms of high blood pressure, cause of swelling, proteinuria, infrequent urine and blood in urine as well as improvements of well-being.

Background: The aging of the world’s population will determine global health trends. According to the 2021 report of the Capital City Health Department, the average life expectancy of the Mongolian population is 71.3 years (male 67.3, female 76.7), the difference between male and female life expectancy is 9.4 years, and elderly people aged 60 and over account for 8.1% of the total population. The report also shows that 6.5% of all outpatient visits are for people aged 60-64, and 9% are for people aged 65 and over, which means that they do not receive adequate health care services. Therefore, it is important to increase the access to and quality of health care services provided to the elderly in order to improve their health and quality of life. Comorbidities that are common among the elderly are one of the pressing issues in the health sector. Aim: We aimed to study the prevalence and risk factors of multi-morbidities among older adults (aged sixty years and over) in urban and rural areas. Materials and Methods: To accomplish our aim, we conducted 156 lifestyle related questionnaires and 18 health related questionnaires among 720 older people in Ulaanbaatar city and rural areas, and created the database. Pearson correlation coefficient was used to determine the relationship between the quantitative influence of factors using single and multi-factor linear (β-coefficient) and binary logistic regression (odds ratio, CI 95%) methods, and p value less than 0.05 was considered statistically significant. The Apriori algorithm in SPSS was used to determine the relationship between multiple chronic diseases in the elderly people. Results: The prevalence of comorbidity was higher in urban areas (48.1%) and rural areas (51.9%), and ageing (urban areas OR: 2.45, 95% CI: 0.9-6.2; rural areas OR: 6.35, 95% CI: 1.47-27.4, P<0.01) was a risk factor of multi-morbidities. Multimorbidity is defined as the presence of 2 or more chronic conditions, and 3, 4, and 5 chronic conditions were co-occurred to older adults with chronic conditions, 28.7% (165). 11 common patterns of relationships in urban areas and 18 common patterns of relationships in rural areas (support (A→B)>3%, confidence (A→B)>30%, lift (A→B)>1) were determined. Conclusion Multimorbidity was different in urban and rural areas, 11 common patterns in urban areas and 18 common patterns in rural areas were determined. It has shown that the prevalence of multimorbidity was different in urban and rural areas.

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.