Using fuzzy ant colony optimization for diagnosis of diabetes disease

Ant colony optimization (ACO) has been used successfully in data mining field to extract rule based classification systems. The Objective of this paper is to utilize ACO to extract a set of rules for diagnosis of diabetes disease. Since the new presented algorithm uses ACO to extract fuzzy If-Then rules for diagnosis of diabetes disease, we call it FADD. We have evaluated our new classification system via Pima Indian Diabetes data set. Results show FADD can detect the diabetes disease with an acceptable accuracy and competitive or even better than the results achieved by previous works. In addition, the discovered rules have good comprehensibility.