An Intelligent Sy
Mohammad Fiuzy *,a,b , Javad Haddadnia a, b, Nasrin Mollaniac, Maryam Hashemian b, Kazem Hassan pourd
a. Department of Biomedical, Faculty of Electrical and Computer, Hakim Sabzevari University, Sabzevar, Iran
b. Research Center for Advanced Medical Technologies, Sabzevar University of Medical Sciences, Sabzevar, Iran
c. Departments of Biology, Faculty of Basic Sciences, Hakim Sabzevari University, Sabzevar, Iran
d.Department. of Clinical Sciences, Sabzevar University of Medical Sciences, Sabzevar, Iran
*Sabzevar, KHorasan Razavi, Iran, Postal Code : 65418-13187
Abstract: Diabetes occurs when the body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important (classification) problem. Diabet Diagnosis is a very problematic issue in medical diagnosis. Nowadays, many relatively complex clinical trials are carried out. Early diagnoses of diabetes dramatically reduce injuries and damage caused by the infection in community. In this study, a method for proper diagnosis based on the optimal features of the Risk Factors in patients is introduced. By Using a combined artificial intelligence methods, including search algorithms (BGA1) to explore or search and select the best features, Data mining methods (FCM2) got to classify and categorize data (patient characteristics led to the diagnosis of non-patient) Neural Network (NN3) for modeling or detection and identification of structural parameters of the disease, diabetic patient has been detected. Then, for better Comparison and show the Performance of the Proposed System, Patients tested based on Eight Factors of World Health Organization (WHO4) to Diabet Diagnosis by the same Intelligent System. The proposed system by using a combination of these methods was successful to achieve 94.031 % precision for diabetic patient identification. Accurate detection by combination and interaction of these methods based on the optimal appearance and Risk features, introduced by the proposed algorithm that Compared with the common methods of detection and diagnosis of patients with one side and artificial methods of the authorities on the other hand, its kind and even more accurate than other methods, the result is a smart combination. It's on operation kind has better than even more intelligent system that had been introduced, given in this document.
[M. Fiuzy, J. Haddadnia, N. Mollania, M. Hashemian, K. Hassan pour. An Intelligent System For Diabet Diagnosis Based on Combined Intelligent Algorithm and Risk Factors in Patients. Life Sci J 2013;10(4s):380-386] (ISSN:1097-8135).http://www.lifesciencesite.com. 57
Keywords: Diabet, Risk Factor, Artificial Intelligent Process.