Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over 30 years old, who have participated in the screening of diabetes plan in Mashhad city that it was done by Mashhad University of Medical Sciences from October to December 2010. According to the fasting blood sugar criteria, 5414 individuals were identified as healthy and 1046 individuals were identified as pre-diabetic. Age, gender, body mass index, systolic blood pressure, diastolic blood pressure and waist-to-hip ratio were measured for every participant. The data was entered into the Microsoft Excel 2013 (Microsoft Corp., Redmond, WA, USA) and then analysis of the data was done in R Project for Statistical Computing, Version R 3. 1. 2 (www. r-project. org). Ordinary logistic regression model was fitted on the data. The outliers were identified. Then Mallow, WBY and BY robust logistic regression models were fitted on the data. And then, the robust models were compared with each other and with ordinary logistic regression model according to goodness of fit and prediction ability using Pearson's chi-square and area under the receiver operating characteristic (ROC) curve respectively. Results: Among the variables that were included in the ordinary logistic regression model and three robust logistic models, age, body mass index and systolic blood pressure were statistically significant (P< 0. 01) but waist-to-hip ratio was not statistically significant (P> 0. 1). There were 552 outliers with misclassification error in the ordinary logistic regression model. Pearson's chi-square value and area under the ROC curve value in the Mallow model were almost the same as for ordinary logistic regression model. But it was relatively higher in BY and WBY models. Conclusion: Based on results of this study age, overweight and hypertension are risk factors of prediabetes. Also, WBY and BY models were better than ordinary logistic regression model, according to goodness of fit criteria and prediction ability.

Most of the studies on phenotype differences, including some diseases, are based on studying some specific positions in the genome called Single Nucleotide Polymorphism (SNP). Some SNPs alone and some by interacting with others, play an important role in any phenotype or specific disease. Various models, including the regression models, are designed and implemented for the prediction of these diseases. In this paper, three penalized logistic models including Ridge, Lasso and Elastic Net (EN), are used to predict the risk of a specific disease, while overcoming the limitation of the classic logistic regression on high-dimensional SNP datasets. The models are implemented on 10000 samples of the SNP datasets of OWKIN-Inserm Institute, which contains 18124 SNPs. Among these three, the Lasso model with minimizer lambda indicate higher accuracy (73. 73%) and AUC (83. 54%). The model is also less complex, since it eliminates less related features as much as possible and keeps only the most informative. Additionally, getting better results with Lasso indicates that multicollinearity is either not existence between variables or is low and can be neglected.

Background and Objectives Angiography is a common and invasive method in diagnosing cardiovascular diseases. Some patients refuse to perform angiography due to reasons such as fear, high cost, and lack of confidence in the decision of physician for angiography. This study aims to determine the factors predicting coronary artery occlusion to predict the outcome of angiography. Subjects and Methods In this cross-sectional study, participants were 1187 patients received angiography in Ghaem Hospital in Mashhad, Iran. Demographic data, lipid profile, blood sugar level, and history of underlying disorders were used in two prediction models of logistic regression and zero-inflated negative binomial (NB), fitted using R3. 6. 1 software. Then, their sensitivity and specificity were compared. Results Of 1187 patients, 404 (34%) had negative angiography. The results of both models showed that the risk of positive angiography was significantly higher in male and diabetic patients. The risk increased with the increase of age. The area under the ROC curve (sensitivity and specificity) for logistic regression and zero-inflated NB models were 78. 4(70. 4%, 70. 5%) and 78. 2(71. 4%, 71. 5%). Conclusion Age, gender, smoking, and history of diabetes are significant predictors of the angiography outcome. There is no significant difference between logistic regression and zero-inflated NB models in predicting the outcome of angiography. Due to the ease of use of logistic regression model, it can be used to predict the results of angiography.

Climatic factors and human activities can cause mass movements. Due to the risk of these movements to human communities, one has to control them. In order to control mass movements, different aspects of the movements should be studied in detail. The aim of this research is to evaluate the efficiency of a statistical model - logistic regression - and a probabilistic model - frequency ratio - in rockfall hazard mapping in the Salavatabad saddle in eastern Sanandaj, Iran. The study of 34 sensitive slopes and 34 stable slopes in the study area using field works, local interviews and the literature review showed that 8 factors including slope gradient, slope aspect, slope curvature, elevation above the sea, and distance from road, distance from faults, lithology and landuse are the most effective factors for occurring rockfalls. The relationships of rockfall and these 8 factors were studied by the logistic regression model and the probabilistic frequency ratio model and the generated rockfall susceptibility maps were compared. The results showed that both models work reasonably well for rockfall hazard studies; however, the logistic regression model has an accuracy of 85.09 percent of the Area under ROC Curve (AUC) in prediction of sensitive areas compared to the accuracy of 76.74 percent by the probabilistic frequency ratio model. Therefore, the logistic regression model can be used for rockfall hazard studies in the study area.

There are some forecasting models derived from logistic growth curves. This paper compares the predictive power of three relatively new models namely, logistic, Harvey logistic, and Harvey models in the prediction of electricity consumption in Iran. These models are employed to calculate and to compare actual and predicted values of domestic, non-domestic and total consumption of electricity in Iran. The results suggest that Harvey model outperforms other models in forecasting Iranian electricity consumption.

The aim of this study was providing plant species predictive habitat models by using logistic regression method. For this purpose, study area conducted in north east rangelands of Semnan modeling vegetation data in addition to site condition in formation including topography, and soil was prepared. sampling was done within each unit of sampling parallel transects and 1 vertical transect with 750m length, each containing 15 quadrates (according to vegetation variations) were established. Quadrate size was determined for each vegetation type using the minimal area method. Soil samples were taken from 0-20 cm and 20-80 cm in starting and ending points of each transect. Logestic regression (LR) techniques were implemented for plant species predictive modeling. To plant predictive mapping, it is necessary to prepare the maps of all affective factors of models. To mapping soil characteristics, geostatistical method was used based on obtained predictive models for each species (through LR method). The accuracy of the predicted maps was tested with actual vegetation maps. In this study, the adequacy of vegetation type mapping was evaluated using kappa statistics. Predictive maps of Astragalus spp. (k=0.86), Halocnemum strobilaceum (k=0.51), Zygophylum eurypterum (k=0.58) and Seidlitzia rosmarrinus (k=0.6) with narrow amplitude is as the same of actual vegetation map prepared for the study area. Predictive model of Artemisia sieberi (k=0.33), due to its ability to grow in most parts of north east rangeland of Semnan with relatively different habitat condition, is not possible.