Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. This paper presents selected membership functions extracted by a fuzzy neural network named NEWFM. The selected membership functions can capture the concentrated and essential information without sacrificing the classification capability. To verify the performance of the NEWFM, the well-known data set of Wisconsin breast cancer is performed. We applied NEWFM model to extract fuzzy membership functions for the UCI antibody deficiency syndrome diagnosis. Then selected features obtained by non-overlapped area measurement method are presented.