  A way to enhance the performance of a model that combines genetic algorithms
and fuzzy logic for feature selection and classification is proposed. Early
diagnosis of any disease with less cost is preferable. Diabetes is one such
disease. Diabetes has become the fourth leading cause of death in developed
countries and there is substantial evidence that it is reaching epidemic
proportions in many developing and newly industrialized nations. In medical
diagnosis, patterns consist of observable symptoms along with the results of
diagnostic tests. These tests have various associated costs and risks. In the
automated design of pattern classification, the proposed system solves the
feature subset selection problem. It is a task of identifying and selecting a
useful subset of pattern-representing features from a larger set of features.
Using fuzzy rule-based classification system, the proposed system proves to
improve the classification accuracy.
