%0 Journal Article %T 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) %A Fiuzy, Mohammad %A Qarehkhani, Azam %A Haddadnia, Javad %A Vahidi, Javad %A Varharam, Hadi %J Journal of Mathematics and Computer Science %D 2013 %V 6 %N 4 %@ ISSN 2008-949X %F Fiuzy2013 %X 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. %9 journal article %R 10.22436/jmcs.06.04.03 %U http://dx.doi.org/10.22436/jmcs.06.04.03 %P 272 - 285 %0 Book %T Diabetes Center, Fact Sheet N°312 %A World Health Organization %D 2011 %I www.who.int/mediacentre/factsheets/fs312/en %C %F Organization2011 %0 Book %T National Diabetes, Fact Sheet %A Centers for Disease Control and Prevention %D 2011 %I www.cdc.gov/diabetes %C %F Prevention 2011 %0 Journal Article %T Diabet Diagnosis By SVM %A M. R. NaiemAbadi %A N. A. A Chamachar %A E. Tahami %A H. Rabbani %J Proceeding of the 14th Iranian student Conference on Electrical Engineering, September 6-8; Kermanshah, Iran. %D 2011 %V %F NaiemAbadi2011 %0 Book %T Diabetes Basics %A American Diabetes Association %D 2011 %I www.diabetes.org/diabetes-basics %C %F Association 2011 %0 Journal Article %T Decision templates for multiple classifier fusion: an experimental comparison %A L. I. Kuncheva %A J. C. Bezdek %A R. P. W. Duin %J Pattern Recognition Journal %D 2001 %V 34 %F Kuncheva2001 %0 Journal Article %T Combining Multiple Classifiers Using Dempster’s rule for text Caregorization %A B. Yaxin %A B. David %A W. Hui %A G. Gongde %A G. Jiwen %J Applied Artificial Intelligence Journal %D 2007 %V 21 %F Yaxin2007 %0 Book %T Combining Pattern Classifiers, Methods and Algorithms %A L. I. Kuncheva %D 2005 %I NY: Wiley Interscience Publisher %C New York %F Kuncheva2005 %0 Journal Article %T Review of Classifier Combination Methods %A S. Tulyakov %A S. Jaeger %A V. Govindaraju %A D. 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Merz %D 1998 %I Irvine, UCI, CA: University of California, Dept. of Information and Computer Science %C archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes %F Newman1998 %0 Book %T Combining rough and fuzzy sets for feature selection %A R. Jensen %D 2005 %I Ph.D. Thesis. School of informatics, Univertsity of Edinburgh %C Engeland %F Jensen2005 %0 Journal Article %T Introduction of an intelligent system for accurate diagnosis of breast cancer %A M. Alipoor %A J. Haddadnia %J Iranian Journal of Breast Disease (IJBD) %D 2009 %V 2 %F Alipoor2009 %0 Journal Article %T Spectral clustering with eigenvector selection %A T. Xiang %A S. Gong %J Pattern Recognition journal %D 2008 %V 41 %F Xiang2008 %0 Book %T A Anovel For Diabet Diagnosis based on Combining Intelligent System such as Fuzzy System %A A. Qarehkhani %A M. Fiuzy %A J. Haddadnia %D 2012 %I Decission Tree, Adaptive Neuro Fuzzy System. 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