Application of modified fuzzy clustering to medical data classification

Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering me thod dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained cl ustering prototypes and leading to final prototypes, which a re used for creating the classifier fuzzy if-then r ules. The main goal of that work was to examine a performance of the cl assifier which uses such rules. Commonly used inclu ding medical benchmark databases were applied. In order to valid ate the results, each database was represented by 1 00 pairs of learning and testing subsets. The obtained classifi cation quality was better in relation to the one of the best classifiers – Lagrangian SVM and suggests that presented clusteri ng w th additional sub-methods are appropriate to a pplication to classification algorithms.