An Application of Logistic Regression Modeling to Predict Risk Factors for Bypass Graft Diagnosis in Erbil

Abstract

In the medical world, predictive models for assessing operative risk using patient risk factors have gained appeal as a useful tool for adjusting surgical outcomes. The goal of this study was to see if there was a link between the severity of atherosclerosis as determined by angiography and changes in several key biochemical, hormonal, and hematological variables in patients who had Coronary Artery Bypass Graft (CABG) surgery. This study included 100 adult patients who had coronary angiography, as well as a standardized case-control study of acute myocardial infarction that included 60 healthy people. In addition, not all investigations of heart attack disorders were concerned with modeling; rather, they were all concerned with classification. A family of Generalized Linear Models called Binary Logistic Regression was used. Because most phenomena' outcomes have only two values (alive/dead, exposed/not exposed, presence/absence and etc.), logistic regression analysis is a common method and plays an important role in health science. Overall, 62.5% of individuals were grouped into surgical bypass grafts, while 37.5% were healthy people. Hemoglobin A1c (HBA1C) was wisely significant, and the odds of one unit increase led to roughly 7.488 times higher. Age and Body Mass Index (BMI) had quite high and substantial effect parameters with a 1.2 times higher likelihood than those who have smaller BMI and younger. According to the study, smokers were more likely to be at risk of undergoing bypass surgery by 4.18 times. However, there was no significant link between gender, screening creatinine, Cholesterol (CHO), Triglycerides (TG), High-density lipoprotein levels (HDL), Lower density level (LDL), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) with the outcome variable.