The Effectiveness of Hope Therapy with Monotheistic Integrated Therapy (MIT) Approach on the Mental Health of the Male Secondary (High) School Students in Yazd County 2 2 Background: Emotional and behavioral disorders as well as social problems may damage individuals’ mental health especially those of adolescent students who are more vulnerable to these issues. This study aims to explore the effect of hope therapy with monotheistic integrated therapy (MIT) approach on the mental health of male high school students. Methods: In this quasi-experimental study, 40 adolescent male students were selected based on convenience sampling from a high school of Yazd County in academic year of 2020-2021. The participants were assigned to two groups including an experimental group (N = 20) and a control group (N = 20). The group counseling course was run for 6 sessions for the experimental group. Child Symptom Inventory-4 (CSI-4) for 6-to-14-year-old children and adolescents, based on Diagnostic and Statistical Manual of Mental Disorders (DSIM-IV), was used for data collection. Covariance was used through SPSS 26 for data analysis. Results: It was found that the difference between the experimental and control groups regarding the dimensions of mental health, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder (ODD) occurred due to experimental intervention (P < 0.000); however, the independent variable did not have a significant effect on the dependent variable with regard to the dimension of behavioral disorder (P = 0.079). Conclusion: Hope therapy pattern with regard to MIT approach (group counseling) is one of the effective approaches on increasing the mental health of male high school students. 0 0 2022/06/8 1401/3/18 2023/12/16 1402/9/25 Abbas Afkhami Aghda Abbas Afkhami Aghda Iran Hassan Zareei Mahmoodabadi Hassan Zareei Mahmoodabadi Iran Ali Hakimizadeh Ardakani Ali Hakimizadeh Ardakani Iran Adolescent health Hope therapy Mental health Monotheistic integrated therapy ## ##

Comparison of Artificial Neural Network and Decision Tree Model Algorithms in Predicting Type 2 Diabetes Classification Status 2 1 2 Background: Diabetes is a metabolic disorder in the body. Using data mining techniques is useful for predicting diabetes, so the aim of this study was to predict diabetes status using artificial neural network and decision tree models. Methods: This study was descriptive and based on secondary data. Data from 4820 individuals were also analyzed. In this study, the performance of two decision tree models and artificial neural networks was compared. Data was randomly divided into three parts, 70% as training, 20% as validation, and 10% as testing. Various criteria such as accuracy, precision, specificity, sensitivity, ROC-AUC curve, and F1-Score were used to evaluate the models, and finally, the best algorithm for predicting diabetes was identified. Results: The decision tree and artificial neural network models were obtained with 97% Precision and 97% accuracy and 96% Precision and 96% accuracy, respectively. The area under the ROC curve in the artificial neural network model (95%) was higher in the training and testing sets than the decision tree model (92%). Conclusion: Although the accuracy of the decision tree model in predicting diabetes status was slightly higher than that of the artificial neural network model, the area under the curve (AUC) of the neural network was higher, and therefore, both models performed well. According to these two models, the variables of fasting blood sugar, systolic blood pressure, and age were effective variables in predicting diabetes status. 18 27 2022/06/82026/03/1 1404/12/10 2023/12/162026/05/5 1405/2/15 Masoud Amiri Masoud Amiri Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran Iran amirim39@yahoo.com Somayeh Hessam Somayeh Hessam Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran Iran Shessam@iau.ac.ir Shaghayegh Vahdat Shaghayegh Vahdat Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran Iran sha_vahdat@yahoo.com Shahram Tofighi Shahram Tofighi 1. Department of Future Studies and Theory Building, Iranian Academy of Medical Sciences, Tehran, Iran 2. National Center for Health Insurance Research, Tehran, Iran Iran shr_tofighi@yahoo.com Prediction Type 2 Diabetes Machine Learning Data Mining 1. N Ahmed, RA, M,M Islam, et al. 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