Iranian Journal of

---

Background and Objectives: Diabetes is a chronic and common metabolic disease which has no curative treatment. Logistic regression (LR) is a statistical model for the analysis and prediction in multivariate statistical techniques. Discriminant analysis is a method for separating observations in terms of dependent variable levels which can allocate any new observation after making discriminating functions. The aim of this study was to compare and determine the effective variables in type 2 diabetes.

Methods: The data included 5357 persons obtained through a cohort study in Kerman, southeastern Iran, in 2009-11. Diabetes was considered the response variable. The independent variables after deleting colinearity and correlated variables included height, waist circumference, age, gender, occupation, education, drugs, systolic blood pressure, HDL, LDL, drug abuse, activities, and triglyceride. Sensitivity, specificity, accuracy, and ROC curve were applied for determining and comparing the prediction power of the models.

Results: The results in the reduced model with extracted significant variables from the full model, the sensitivity of the LR model and DA was 74% and 22.4%, the specificity of the LR model and DA was 71.1 % and 95.4 %, the prediction accuracy of the LR model and DA was 71.5% and 85.3%, and the ROC curve of the LR model and DA was 80.3% and 80.1%, respectively.Simulation showed the sensitivity, specificity, accuracy, and ROC curve was 99.18%, 98.49%, 98.59%, and 99.9% for the LR model and 92.62%, 99.19%, 98.26%, and 99.56% for DA, respectively.

Conclusion: The results showed that the risk factors of diabetes in the logistic regression reduced model were waist circumference, age, gender, LDL level, systolic pressure, and drugs. Also, the sensitivity of the LR model was more than DA while DA had a higher specificity and prediction accuracy. Comparison of the ROC curve showed that the prediction estimated values were rather similar in both models, but the two models were the same asymptotically.

---

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.

---

Background and Objectives: Gestational diabetes mellitus (GDM) is a medical problem in pregnancy, and its late diagnosis can cause adverse effects in the mother and fetus. The purpose of this research was to estimate the accuracy parameters of a biomarker for early prediction of gestational diabetes in the absence of a perfect reference standard test.
 
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
 
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively. 
 
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.