Coronary artery bypass grafting (CABG) is one of the most common heart surgeries. Most patients are elderly with comorbidities that affect morbidity and mortality rates. Even in developed countries mortality rate is 1.9-5.3%. Determination of perioperative risk factors and creating protocols to take precautions accordingly may reduce mortality in patients. In CABG patients, due to the surgical burden combined with existing comorbidities, it is important for physicians to evaluate the risk status and predict the mortality rates. For this purpose, various scoring systems have been developed. Fuzzy logic method allows partially membership, so that a glass that is neither full nor empty can be numerically expressed as being partially full but also partially empty. This feature is similar to the reasoning structure of the human brain that uses linguistic tools. In this way, in cases where there is no precise mathematical model and where the existence of imprecise and uncertain information is natural, such as medical applications, in order to solve the problem, fuzzy easily allows linguistic expressions containing the knowledge, experience and intuition of expert to transforme into the model created for the solution. In decision making process, fuzzy logic provides ability to use linguistic expressions, including experts' intuition. Thus, decisions can be made even with approximate data and uncertainty. For this reason, fuzzy logic is used in a wide range of research from engineering to medicine. The records of patients who underwent CABG surgery in Istanbul University-Cerrahpaşa Cardiology Institute between January 1, 2020 and July 31, 2024 will be examined. Preoperative major risk factors diabetes, pulmonary, neurological, kidney and liver disease with preoperative minor risk factors age, weight and smoking will be recorded. Perioperative risk factors; duration of artificial circulation, number of vessels bypassed, and number of blood products used will be recorded. Our primary aim is to create a fuzzy logic-based perioperative risk classification model to identify high-risk patients in CABG surgery. Secondly we aimed to investigate the effect of perioperative risk factors on postoperative complications