An Intelligent Fuzzy System for Diabetes Disease Detection using Harris Hawks Optimization

This paper proposes a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD), and then the fuzzy membership functions are tuned using the Harris Hawks Optimization (HHO). In the experimental dataset, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy, which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.