COST EFFECTIVE APPROACH ON FEATURE SELECTION USING GENETIC ALGORITHMS AND FUZZY LOGIC FOR DIABETES DIAGNOSIS

A way to enhance the performance of a model that combines genetic algorithms and fuzzy logic forfeature selection and classification is proposed. Early diagnosis of any disease with less cost ispreferable. Diabetes is one such disease. Diabetes has become the fourth leading cause of death indeveloped countries and there is substantial evidence that it is reaching epidemic proportions in manydeveloping and newly industrialized nations. In medical diagnosis, patterns consist of observablesymptoms 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 selectionproblem. It is a task of identifying and selecting a useful subset of pattern-representing features from alarger set of features. Using fuzzy rule-based classification system, the proposed system proves toimprove the classification accuracy