Background Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors. Methods This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance. Results 50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS. Conclusion Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

No abstract

Background and Purpose: Disasters and traffic accidents as the leading causes of disability and death throughout the world are the most significant health problems which have usually been predictable and, therefore, possible to prevent. The present study, as the first attempt, was conducted to calculate the burden of life years lost due to early death caused by traffic accidents in Mazandaran Province. Materials and Methods:The current study was cross-sectional, and the data was collected from the center of Mazandaran Legal Medicine. The number of years of life lost due to premature death was calculated by using the instructions GBD2010 age and gender composition of the province was taken in the last census in 2012 from the Statistical Center. Then the SPSS Software was used to key in all the collected information in order to perform the analysis. Results: Of the total population in 2015, 729 deaths were recorded due to car accidents with 77.9 percent of them being male. Mean age was 43.07±21.18 and 44.67±23.34 in women. The number of years of life lost due to premature death was 24972.7 years in men, 6965.3 years in women, and the total of two genders was 31938 years (10.6 years per thousand people) which were calculated, and it was the highest in the age group ranging from 20 to 24 years old. Discussion: According to the high rate of deaths from traffic accidents and damages resulted from it, and in order to reduce these losses, it is necessary to take appropriate preventive measures.

Introduction: Depression is one of the psychiatric disorders and is the most common mood disorder. Stably and sometimes unstable, depression can involve and interfere with different aspects of life. By disrupting tasks, reducing motivation, causing anxiety, fear, and concern, depression impairs a significant part of the intellectual ability. Complications of depression have been proven on presence and absenteeism, accuracy in performing duties and efficiency. This study tends to determine the prevalence of depression among employees of Lorestan University of Medical Sciences and its relationship with demographic variables in 2017.Method: This cross-sectional study was conducted on employees of the Lorestan University of Medical Sciences in 2017. The subjects were 270 people who filled in adult BDI-II (including 21 3-point questions). The inventory is scored from 0 to 63(0- 13 minimal depression (normal)), 14-19 mild depression, 20-28 moderate depression, and 28-63 severe depression). Data were analyzed using SPSS version 23.Results: The mean depression score was 10.7; 48.1% had depression. Prevalence of depression was 12.6% mild depression, 11.1% moderate depression and 6.3% severe depression; 89 (33%) were single and 181 (67%) were married; Prevalence of depression was 29.47% in women and 30.28% in men.Discussion: Men were more likely to develop depression than women (27%), which is contrary to many reports. This study showed a significant relationship between age and prevalence of depression. There was a significant relationship between prevalence of depression and marital status; there was a significant relationship between workplace and the kind of work done by people and depression. There was a significant relationship between parental education and depression. There was no significant relationship between education and depression. However, some studies did not report this significant relationship.International Journal of Human and Health Sciences Vol. 03 No. 01 January’19. Page : 14-18

Background: Anxiety disorders are the most common groups of mental disorders. Based on the world health organization reports 7.4% of global DALYs are caused by disorders, which are in the mental and behavioral category. One of the problems of these patients is the length of stay in the hospital, which can be studied in various aspects. Objectives: The objective of this study was to determine the effects of related factors on the duration of hospitalization in patients with anxiety disorders. Methods: A historical cohort of patients placed in the psychiatric center in Sari, northern Iran, were studied from April 2007 to March 2012. Statistical analysis using Weibull regression and stata.12 was performed at the significant level of 0.05. Results: A total of 427 persons were studied. The median length of hospital stay was 17 (inter quartile range 10 -29 day). The results showed age was associated with the length of stay (P = 0.036). Also, patients with previous hospitalization and patients who received electro convulsive therapy and occupational therapy had a longer stay in the hospital (P < 0.001). Conclusions:The overall results showed that the type of treatment is effective in reducing the duration of hospitalization. Aging has a subtractive effect on the length of hospital stay. It seems that additional research concerning mental health care services may be required to identify more factors affecting the length of stay.