Automated creation of transparent fuzzy models based on decision trees--application to diabetes diagnosis

In this paper we propose a novel approach for the simplification of a fuzzy model. Initially, we employ a methodology for the automated generation of fuzzy models based on decision trees. The methodology is realized in three stages. Initially, a crisp model is created from a decision tree, induced from the data. Then, the crisp model it is transformed to a fuzzy one. Finally, in the third stage, all parameters entering the fuzzy model are optimized. The simplification technique is based on the pruning of the initial decision tree. The proposed approach is applied for diabetes diagnosis and the obtained results indicate its efficiency and effectiveness.