Title: Three-Term Backpropagation Network Based On Elitist Multiobjective Genetic Algorithm for Medical Diseases Diagnosis Classification
Abstract: Recently, the problems related to intelligent medical disease diagnosis classification have become one of the important areas of study. Therefore, this paper proposes a new intelligent classifier approach, by using the Three-Term Backpropagation (TBP) network based on the Elitist Multiobjective Genetic Algorithm (MOGA). One of the recent MOGAs is a Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is used to reduce or optimize the error rate and network structure of TBP simultaneously to achieve more accurate classification results. In addition accuracy, sensitivity, specificity and 10-fold cross validation are used as performance evaluation indicators to evaluate the outcome of the proposed method. The proposed intelligent methodology is applied in four kinds of standard medical diseases datasets, obtained from the University of California at Irvine (UCI) repository. The results illustrate that our approach is viable in medical diseases diagnosis classification when compared with some other methods found in the literatures. (Ashraf Osman Ibrahim, Siti Mariyam Shamsuddin, Nor Bahiah Ahmad, Sultan Noman Qasem. Three-Term Backpropagation Network Based On Elitist Multiobjective Genetic Algorithm for Medical Diseases Diagnosis Classification. Life Sci J 2013;10(4):1815-1822). (ISSN:1097-8135). http://www.lifesciencesite.com . 238
Publication Year: 2013
Publication Date: 2013-11-25
Language: en
Type: article
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Cited By Count: 9
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