Fuzzy Classification Rules Generation with Ant Colony Optimization for Diabetes Diagnosis

Classification systems have been used to discover patient's data patterns and construct predictive model. Fuzzy optimization approaches have become well-known solutions for the classification problems as this improves classification and decision support systems by lap over class definitions and their powerful abilities to handle ambiguity and fuzziness .Ant Colony Optimization (ACO) is a probabilistic search algorithm for large scale optimization problems and used frequently in real-time applications. The combination of Ant Colony Optimization and Fuzzy Logic enables to detect diabetes disease accurately and employ the final discovered fuzzy rule base in the development process of a diabetes disease detection expert system effectively