Abstract
Background: Type 2 diabetes is one of the most common diseases among people. Early diagnosis andtreatment can reduce mortality and morbidity. So far, various solutions have been proposed to predict thistype of disease.Materials and Method: In this paper, a method for diagnosing diabetes was proposed using the AntColony Optimization (ACO) algorithm. To this end, data set properties are first reduced using artificialneural network features and then prepared for classification purpose. Finally, some components of accuracyassessment on the proposed system were calculated.Results: The simulation results show that by adjusting the parameters of ANN and ACO, about 3.2% betterprediction accuracy is obtained than other researches.Conclusion: The results of experiments represent that the proposed method is proper for health managementin diabetes.
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