جستجوی مقالات مرتبط با کلیدواژه « مدل متوسط بیزی » در نشریات گروه « پزشکی »

Diabetes mellitus is one of the most common metabolic disorders in the world. It has many risk factors, and the prediction of these factors is important to prevent it. The current study aimed at determining the factors associated with the incidence of type 2 diabetes using the Bayesian regression model of spike and slab.
In the current descriptive study, data from 819 participants in the diabetes screening program at Zahedan Health Centers, Iran in 2014 were analyzed. The convenience sampling method wasused. The demographic information was collected from patient's records, including gender, age, history of blood pressure,ect., and fasting blood glucose level. For prediction and variable selection, the Bayesian regression model of spike and slab was used, then validity of the model was assessed by mutual validation method. Data analysis was performed with R software version 3.3.2.
The family history of diabetes and age were significantly correlted with diabetes in the subjects;the family history of diabetes with a 100% reliability coefficient was the most effective variable.
The Bayesian regression model has a good ability to dignose diabetes; while recognizing less effective variables, the predicting ability of the model was also maintained. Given the factors associated with the incidence of diabetes, the implementation of screening plans with priority given to the elderly and those with a family history of diabetes is suggested to control the outbreak of diabetes.

Diabetes is a chronic disease which usually begins with impaired glucose tolerance. This step is known as pre-diabetes. People with pre-diabetes are at greater risk for diabetes. Typically for the variable selection, stepwise approach is used which does not take into account model uncertainties. In this study, Bayesian Model Averaging (BMA) method was used to sort out the above shortcoming.
The study population was 734 pre-diabetic women with 20 years and older participated in Tehran Lipid and Glucose Study (TLGS). In this study, the stepwise and BMA variable selection methods were employed in logistic regression. Then area under curve (AUC) for both methods was computed and compared with Delong test. All analyses was done using R version 3.1.3.
BMA selected the fasting plasma glucose, 2 hours’ blood glucose, and family history of diabetes, body mass index and aspirin use at baseline as risk factors for diabetic. In addition to these factors, stepwise method selected diastolic blood pressure, history of past 3 months’ hospitalization, thyroid drug use and education. Although the number of variables selected by BMA (5 variables) was less than that of stepwise (9 variables), AUC for the two methods was not significant.
It seems that the BMA provide better model for screening of diabetes because with selecting fewer variables, prediction ability of the model is preserved