Predicting Risk Factors of Bypass Graft Data in Erbil Using the Application of Bayesian and Non-Bayesian Methods

Coronary heart disease can be defined as a disease that plaque (a waxy substance) constructs inside the coronary arteries. The purpose of this study was to determine risk factors affecting Bypass Graft surgery. In this research, 100 adult patients underwent coronary angiography at Erbil Cardiac Center, and data was obtained from them. Patients were followed up from January 2016 until the end of December 2020. Data was also obtained from 60 healthy people who underwent the same coronary angiography. Multiple logistic regression was used to determine factors affecting Bypass Graft surgery. Variables were selected using the forward selection method. We have found that HbA1c, Age, WBC, BMI, Eosinophil, Blood Sugar, DBP, PLT, MCH, and T3 variables have a relatively interesting connection with the Bypass Graft using visual inspection. The significant variables contributing to the prediction of for Bypass Graft operation using multiple logistic regression are DBP, Age, MCH, WBC, and Blood Sugar. The risk factors associated with Bypass Graft surgery using Bayesian logistic regression were Age, WBC, and MCH. In conclusion, there are different results we have achieved between classical and Bayesian logistic models. Further study needed using larger sample size and including informative prior distribution into the Bayesian model.