Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, restricted interests, and repetitive behaviors. Evaluating short-term treatment progress and outcomes in children with ASD is important for determining subsequent treatment goals. Aim: This study aimed to evaluate the outcomes during the first 3 months of applied behavior analysis (ABA)-based intervention using changes in Autism Treatment Evaluation Checklist (ATEC) scores. Materials and Methods: A prospective observational study was conducted among 21 children with ASD. Caregivers completed the 77-item ATEC before initiation of treatment and again after 3 months. Scores were calculated for four ATEC domains: Speech/Language/Communication, Sociability, Sensory/Cognitive Awareness, and Health/Physical/Behavior. Result: The mean age of participants was 5.51±1.45 years, and 16 children (76.19%) were male. Mean baseline Clinical Global Impressions–Severity (CGI-S) score was 4.71±0.72, and participants received a mean of 10.00±1.38 hours of ABA-based intervention per week. Mean total ATEC score decreased from 72.71±7.60 at baseline to 67.14±7.81 after 3 months, corresponding to a mean change of -5.57±2.18 points (-7.66%; t=11.706, p<0.001, d=2.55). Speech/Language/ Communication decreased by 7.05%, Sociability by 6.96%, and Sensory/Cognitive Awareness by 6.56%. The largest relative change was observed in the Health/Physical/Behavior domain (-1.90±1.14; -9.80%; p<0.001). Wilcoxon signed-rank tests were significant across all ATEC domains (p<0.001). Sixteen children (76.19%) met the study-defined criterion of more than a 5-point reduction. Baseline CGI-S was correlated with pre-post ATEC change using Pearson correlation (r=0.466, p=0.033), whereas the corresponding Spearman analysis was not statistically significant (ρ=0.349, p=0.121); therefore, this association could not be considered a robust predictor. Weekly ABA treatment hours were not significantly associated with ATEC change (r=-0.183, p=0.427), and age was also not associated with change (r=-0.043, p=0.855). Conclusion Changes across all ATEC domains were observed during the period in which children received ABA-based intervention, suggesting that some treatment-related changes may be detectable over a relatively short period in everyday settings. However, because only two assessment points were available, the rate and sequence of change could not be determined. Future studies should include larger samples, a comparison group, repeated assessments over 6–12 months, and independent developmental and clinical outcome measures.

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: 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.