Introduction of a Method to Diabetes Diagnosis According to Optimum Rules in Fuzzy Systems Based on Combination of Data Mining Algorithm (d-t), Evolutionary Algorithms (aco) and Artificial Neural Networks (nn)


Authors

Mohammad Fiuzy - Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran. Azam Qarehkhani - Department of Computer, Khorasan Razavi, Neyshabur,Science and Research Branch, Islamic Azad University, Neyshabur, Iran. Javad Haddadnia - Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran. Javad Vahidi - Department of Applied Mathematics, Iran University of Science and Technology, Behshahr, Iran. Hadi Varharam - Ministry of Science, Research and Technology, Tehran, Iran.


Abstract

In time diagnosis of diabetes significantly reduces damages and inconveniences of this disease in society. It may be said that one of the most important problems of diagnosis methods of this disease, particularly in early phases, is not to pay attention to proper features in order to diagnose the disease and as a result weakness in disease diagnosis. This research endeavors to introduce a new method for accurate diagnosis of this disease through usage of a combination of artificial intelligent methods such as fuzzy systems for immediate and accurate decision making, Evolutionary Algorithms (ACO1) for choosing best rules in fuzzy systems, and artificial neural networks for modeling, structure identification, and parameter identification. The proposed system relying on features of database in the form of combination and interaction succeeded in reaching an accuracy of 95.852% which in comparison to current methods on the one hand and to artificial methods in foresaid references on the other hand, has a proper and very faster performance than other intelligent methods and you can see its accuracy and excellence as an intelligent system.


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