This paper presents a methodology for knowledge discovery in data mining of medical data with the use of
hybrid Evolving Fuzzy Neural Networks ( EFuNNS). EFuNNs are five layer sparsely connected networks. EFuNNs
contain dynamic structures that evolve by growing and pruning of neurons and connections. EFuNNS merge three
supervised classification methods: connectionism, fuzzy logic, and case-based reasoning. By merging these strategies,
this new structure is capable of learning and generalising from a small sample set of large attribute vectors as well as
from large sample sets and small feature vectors. After classification has been made through EFuNNs , one can inspect
each class of the patterns acquired. There are several methods of inspections. The easiest one is Statistical Analysis
(SA) of each class. Using central tendency and dispertion statistical measures one can form several rules that govern
each class attributes. The proposed methodology provides fast and accurate adaptive learning for generated rules from
data mining. It is also applicable for classification problem.