A Novel System to Classify Risk Factors to Predict Outcomes after Surgery Using Fuzzy Methods

The aim of this study is to develop a system to recognize most important risk factors which can help to predict outcomes such as mortality or morbidity before performing the specific surgery by the integration a standard assessment checklist based on theoretical considerations and methodological aspects to evaluate the study quality of each publication, fuzzy analytic hierarchy process (FAHP) to order the risk factors, and fuzzy c-means clustering in order to classification. A fundamental idea of this study for applying FAHP was to consider scoring systems as experts to decision making and use of triangular fuzzy number (TFNs) to represent pairwise comparison of odd ratios in order to capture the vagueness. To illustrate the system implementation, the information of scoring systems developed to predict early mortality after CABG (Coronary artery bypass graft) is considered as input and the reasonable results are concluded. This implementation demonstrates the effectiveness and feasibility of the proposed system.