Iranian Journal of

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Background & Objectives: Orthodontic treatment need and demand in 12-year-olds in Abadeh city has not previously been analysed in relation to geographic origin. The purpose of this study was to assess the12 year old students need and demand for orthodontic treatment.
Methods: Four hundred seventeen 12-year-old students was selected from public and private schools in Abade (Fars province, Iran). All the students were examined according to the AC and DHC component of Index of Orthodontic Treatment Need (IOTN) by a trained dentist. Students' and parents' perceived needs were also assessed using AC component and their demand for orthodontic treatment was asked through a questionair .
Results: Twenty two percent of the students were in "no need of treatment" group when assessed by DHC component, 29.5 % were in "average need" and 48.2% were in "definite need" group. When assessed by AC score, these percents were 61.9%, 29%, and 9.1%. Parents and students percieved need for definite orthodontic treatment according to AC score was 8.6% and 7.7%, respectively. The students and their parents’ demand for treatment were 40.6% and 44.9%, respectively.
Conclusion: Orthodontic treatment need in Abade is higher in comparison with other reports according to DHC. DHC is not correlated with orthodontic treatment demand of 12 years old students, but AC had a strong relationship with treatment demand.

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Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these models.
 
Methods: The medical files of 420 pregnant women (2010-12) in Kermanshah health centers were evaluated using convenience sampling. Demographic data, pregnancy-related variables, lab tests results, and a diagnosis of GDM according to a fasting blood sugar level of 92 or more were collected from their files. After fitting the four models, the performance of the models was compared and according to the criteria of accuracy, sensitivity and specificity (based on the ROC curve), the superior model was introduced.
 
Results: Following the fitting of LR, DA, DT and perceptron ANN models, the following results were obtained. The accuracy of the above models was 0.81, 0.83, 0.78 and 0.83, respectively, the sensitivity of the models was 0.50, 0.63, 0.58 and 0.58, the specificity of the models was 0.96, 0.93, 0.87 and 0.94, and the area under the ROC curve was 0.86, 0.78, 0.73 and 0.87, respectively.
 
Conclusion: In predicting and categorizing the presence of GDM, the ANN model had a lower error rate and a higher area under the ROC curve compared to other models. It can be concluded that this model offers better predictions and is closer to reality than other models.