Discretization of Continuous Attributes on Decision System in Mitochondrial Encephalomyopathies

In this work we check how the automatic discretization algorithms generate decision rules for the concrete medical problem - diagnosing mitochondrial encephalomyopathies (MEM). We describe several algorithms for discretization - local and global - of continuous attributes obtained in the second stage of diagnosing MEM. All of these algorithms act together with the data analysis method based on the rough sets theory. This work compares results -- quality of classification rules -- which were obtained using different discretization methods of the continuous attributes.