Comparative Analysis of Fuzzy Logic-based Risk Evaluation Models

In risk management systems the risk factors are described both in qualitative and quantitative terms, therefore models have to be developed that can handle both input types. Since these systems are full of uncertainty and qualitative description the system parameters can be represented in linguistic form, using soft computing - for example fuzzy-based - methods. These techniques are suitable for representing the system outputs with more realistic and user-friendly result. The models analyzed in the paper define the risk level of physical exercise-based fuzzy approximate reasoning systems and structural risk management models. Clustering of risk factors and using hierarchical decision structure in these models makes the evaluation simplified and easy expandable. The models were analyzed in Matlab environment, using input data selected from the theoretical parameters of several groups of patients. Based on this test the novel theoretical model is suitable for comparison with previously implemented models developed with algorithms by others in order to validate our model. The long-term goal is the real-time risk assessment for the model in question while finding the appropriate sensors and its systematization is in progress.