Fuzzy Logic Preanesthetic Risk Evaluation of Laparoscopic Cholecystectomy Operations

BACKGROUND AND OBJECTIVE: Pre-operative risk classification of patients undergoing anesthesia is an essential interest and has been the focus of many research and categorizations. On the other hand, the ideal categorization system, based on medical doctors' clinical experience and cooperation with other disciplines, has not been developed yet. METHODS: In this study, 218 consecutive patient undergoing laparoscopic cholecystectomy operations were included. A novel fuzzy logic evaluation model consisting of 270 rules was constructed. Five major (pulmonary, cardiac, diabetes mellitus and renal or liver disease) and three minor criteria (patients' age, cigarette smoking and body mass index) were chosen to be used during high-risk groups determination. RESULTS: The verification of the success of risk value decision with the proposed novel fuzzy logic algorithm is the main goal of this study. On the other hand, though not essential aim, a statistical consistency check was also included to have a deeper understanding and evaluation of the graphical results. During the statistical analysis the 0-30%, 30-60% and 60-90% risk ranges were found to be in a very strong positive relationship with complication occurrence. In this study, 172, 31, 15 patients were in 0-30, 30-60 and 60-90% risk ranges, respectively. Complication rates were 7/172 (4.07%) in 0-30% range, 3/31 (9.68%) in 30-60% range; and 2/15 (13.33%) in 60-90% range. CONCLUSIONS: Fuzzy based risk classification model was successfully used to predict medical results for patients undergoing laparoscopic cholecystectomy operations and reliable deductions were reached.