Iranian Journal of

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Normal 0 false false false EN-US X-NONE AR-SA Background & Objectives: Tuberculosis (TB) is an important issue which its control is still unsatisfactory at global level. Traditional diagnostic techniques for active TB diagnosis are inadequate: the diagnostic gold standard is the cultural exam which suffers from lengthy processing and requires highly specialized laboratories. Nowadays more specific tests have been recommended. The aim of this study is to evaluate the performance of Quanti FERON-TB (QFT)Gold In Tube-Test as a substitute for specific test tuberculin skin test for diagnosis of latent tuberculosis infection in high risk groups.
Methods: One hundred thirty four (134) individuals who worked in Bo-Ali hospital (Zahedan) enrolled in this study. They had no active tuberculosis. TST and QFT tests were performed. The cut-off point of TST was considered based on 15 (mm) or more indurations as positive. The result of QFT was evaluated by manufactured guidelines. Multivariate logistic regression was used to identify the putative risk factors of positive tests.
Results: Proportion of employees with latent TB were 111(82.8%) were positive by either TST or QFT, and 76(56.7%) were positive by both tests. Agreement between the tests was high (73.8%, k=0.39 95%CI:
0.21-0.44). Positive family history of Tuberculosis was significant risk factor for both positive tests. 
Conclusion: This study showed high latent tuberculosis infection prevalence in hospital workers and high agreement between TST and QFT. Decision to select one of the tests will be depended on the population, purpose of study and availability of resources. The results revealed that the QFT can be appropriate alternative test for high risk group.  

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Background & Objectives: In recent years, different decision support systems (DSS) have been used to predict and diagnose diseases. The purpose of this paper was to compare some DSSs and to evaluate their accuracy in predicting diabetes. 

Methods: In this research, determination and optimization of the weights of the neural network were undertaken using genetic algorithm and Levenberg-Marquardt (GALM). Traditional and K-Fold Cross Validation were used to verify the models. Finally, the proposed model (i.e. GALM) was compared using logistic regression and genetic algorithm based on area under curve (AUC), and Confusion Matrix.

Results: After evaluating the results, the model based on the GALM algorithm showed better sensitivity and specificity in comparison with models based on the logistic regression (LR) and genetic algorithm (GA). Furthermore, among other models, the proposed model had a high sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and a small negative likelihood.

Conclusion: The results showed that the GALM model with a sensitivity, specificity, PPV, NPV, and AUC of 98.7, 90.01, 91.8, 98.3 and 0.979 respectively was an appropriate model for predicting diabetes in comparison with models of GA and LR.

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Background and Objectives: We may sometimes measure the joint effect of correlated independent variables on several dependent variablesThe present study aimed to evaluate the performance of multivariate analysis of variance (MANOVA) and structural equation modeling (SEM) on complex relationships between variables.
Methods: The present study evaluated the knowledge and attitude of 15-18 year-old individuals towards narcotics (glass, ecstasy). The effect of independent variables on two latent variables of knowledge and attitudes was studied using SEM and MANOVA modelingThe mean square error of methods were compared.
Results: The direction of associations was similar in both methods but their coefficients and p-values were different. only the effect of gender (P-value= 0.007) on knowledge in both methods was significant. Nevertheless, gender (P-value < 0.001) and marital status (P-value< 0.001) were significantly associated with  attitude in both methods. The mean square error of multivariate analysis of variance and structural equation modeling was 0.98 and 0.002 respectively.
Conclusion: In the current studythe performance of SEM was better than MANOVA. Therefore, it is suggested that SEM to be used to study the multifactorial  relationship between variables. In addition, only gender was effective on knowledge in both methods, while gender and marital status were effective on attitude in both methods.

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