Multicriteria fuzzy classification procedure PROCFTN: methodology and medical application |
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| Institution: | 1. National Research Council of Canada, Institute for Information Technology-e-Business, e-Health group, P.O. Box 69000, 127 Carleton Street, Saint John, NB, Canada E2L 2Z6;2. Montreal Chest Institute, Immunodeficiency Service, Room 817, McGill, University Health Centre, Canada;1. Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, United States;2. King Saud University, Riyadh, Saudi Arabia;3. Computer Engineering Dept, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;1. Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa, Ontario K1R 6N5, Canada;2. Faculté des Sciences Administratives, Université Laval, Ste-Foy, Québec G1K 7P4, Canada |
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| Abstract: | In this paper, we introduce a new classification procedure for assigning objects to predefined classes, named PROCFTN. This procedure is based on a fuzzy scoring function for choosing a subset of prototypes, which represent the closest resemblance with an object to be assigned. It then applies the majority-voting rule to assign an object to a class. We also present a medical application of this procedure as an aid to assist the diagnosis of central nervous system tumours. The results are compared with those obtained by other classification methods, reported on the same data set, including decision tree, production rules, neural network, k nearest neighbor, multilayer perceptron and logistic regression. Our results are very encouraging and show that the multicriteria decision analysis approach can be successfully used to help medical diagnosis. |
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