Background: Midwives provide essential maternal, neonatal, reproductive and sexual health care and save lives. In our country, in 2017, the job description of midwives was revised and approved by the Minister of Health in accordance with the core competencies established by the International Federation of Midwives by Order A/216. Therefore, it is necessary to study the extent to which midwives are performing the duties included in the revised job description. Aim: To assess of the professional role and performance of midwives in Mongolia five years after the updated midwifery job description was declared. Materials and Methods: There were 1119 midwives working at all levels of the facilities national wide, and 589 respondents were analyzed. Data were collected using a questionnaire, which was developed with input from an obstetrics and midwifery care advisory group, and was distributed online through Google Forms. We developed a questionnaire on the daily professional roles and performances of midwifery services and skills that were part of the job description of midwives in Mongolia. The questionnaire consisted of three parts and were scored based on a Likert scale. This study was approved by the research ethics committee of the School of Nursing (protocol №22/07-b), Mongolian National University of Medical Sciences (MNUMS), and Ministry of Health. Result: 62.3% of the participants had a bachelor’s degree, 34.3% had a professional degree, 93.5% worked as midwives at all levels of health care, 73.4% worked according to their job description, but 21.9% were unable to work according to their main duties; 56% considered that they faced some difficulties in performing their duties at work. Within the scope of maternity care services mentioned in the job description, 71.4% of them often provide monitoring and counseling to normal, low-risk pregnant women according to guidelines (mean 3.07± 0.96); childbirth and postpartum care are occasionally (mean 2.70±1.10) or not regularly provided; and the average ratings of reproductive health, family planning services, and emergency obstetric care interventions are 2.32±1.120 and 2.40±0.040, respectively. According to self-assessment of professional skills, the average score for the ability to provide care during pregnancy was 3.10±0.038, which is sufficient; the ability to provide care during childbirth and postpartum was 2.83±0.037, which is above average; and the ability to provide emergency obstetric care and reproductive health and family planning care was 2.77±0.84 and 2.57±0.890, which is moderate, respectively. Conclusion The education and professional level of midwives working at all levels of the health sector has improved. While midwives frequently provide the pregnancy care outlined in their job descriptions, they only occasionally perform duties related to childbirth, postpartum and reproductive health, family planning, and obstetric emergency care. According to their self-assessments, midwives demonstrate sufficient competence in teamwork and pregnancy care; above-average competence in childbirth and postpartum care; and moderate competence in obstetric emergency care, reproductive health, and family planning services.There is a need to improve the involvement of midwives in certain professional activities.

Background: Likert-scale questionnaire data is widely used to evaluate satisfaction; however, in practical applications, both high classification accuracy and model interpretability are essential requirements. Rule-based machine learning methods provide the opportunity to satisfy both criteria simultaneously. Despite the rapid growth of research using machine learning for classification and prediction in recent years, there is a scarcity of studies employing the C5.0 classification algorithm for Likert-scale satisfaction data, which serves as the rationale for this study. Aim: To implement the C5.0 classification algorithm using a 10-fold cross-validation method to predict two levels of satisfaction (High/Medium) and to evaluate the model’s performance and interpretability. Materials and Methods: he study was conducted at the Department of Physics and Informatics, School of Bio-Medicine, MNUMS, utilizing satisfaction survey data from graduates majoring in Health Information Technology. Data consisting of four Likert-scale variables (V1–V4) were used. The C5.0 model was trained using a parametric grid search and evaluated within a 10-fold cross-validation framework using accuracy and a confusion matrix. Furthermore, IF–THEN rules were extracted from the final rule-based model, and the decision logic was visualized in the form of a flowchart. Results: The results indicated that the rule-based C5.0 model was selected as the optimal model, demonstrating stable performance throughout the cross-validation process. The confusion matrix results showed high precision in identifying the “High” satisfaction class. Among the generated rules, it became evident that variables V4 and V2 have the most significant influence on determining satisfaction levels. Conclusion Utilizing the C5.0 algorithm within a cross-validation framework enables effective and intelligible classification of satisfaction based on Likert data. It was confirmed that the rule-based structure offers distinct advantages for model interpretation and decision-support applications.

Background: Diabetes mellitus is a chronic metabolic disorder with an increasing global prevalence. Early detection and risk assessment are essential for preventing disease-related complications. In recent years, machine learning methods have been increasingly investigated for predicting disease status using medical data and supporting clinical decision-making. Aim: This study aimed to develop a predictive model for diabetes status using the Random Forest classification algorithm based on the PIMA Indians Diabetes Database and to evaluate its classification performance. Materials and Methods: The study used the PIMA dataset, comprising 768 observations, eight predictor variables, and one target variable. Zero values in selected measurements that were physiologically implausible were treated as missing values and imputed using the median. The dataset was split into training and test sets at an 80:20 ratio. Hyperparameters of the Random Forest model were optimized using 10-fold cross-validation on the training data. Model performance was evaluated using Accuracy, Sensitivity, Specificity, Balanced Accuracy, Cohen’s Kappa, and the area under the receiver operating characteristic curve (ROC-AUC). Variable importance was also assessed. All statistical analyses and machine learning procedures were performed in the R programming environment. Results: Cross-validation identified mtry=4 as the optimal configuration, achieving an Accuracy of 78.05% and a Cohen’s Kappa of 0.5062. On the test set, the model achieved an Accuracy of 72.55%, Sensitivity of 58.49%, Specificity of 80.00%, Balanced Accuracy of 69.25%, and Cohen’s Kappa of 0.3883. The area under the ROC curve (AUC) was 0.7802, indicating that the model could distinguish between individuals with and without diabetes better than random classification. Variable importance analysis identified blood glucose level as the most influential predictor, followed by body mass index (BMI), age, and number of pregnancies. Conclusion The Random Forest algorithm demonstrated the potential to predict diabetes status using the PIMA dataset; however, its sensitivity on the test set was relatively low. Therefore, the model should be considered a potential tool for preliminary risk assessment and supporting early detection rather than a replacement for clinical diagnosis. Further research is needed to improve sensitivity, compare the model with other machine learning algorithms, and validate its performance using larger and more diverse datasets.

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.