A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques |
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Authors: | Elpiniki I Papageorgiou |
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Affiliation: | 1. Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland;2. Faculty of Economics and Informatics in Vilnius, University of Bialystok, Kalvariju G. 135, LT-08221 Vilnius, Lithuania;1. Department of Informatics and Computer Technology, Technological Educational Institute (TEI) of Lamia, 3rd Km Old National Road Lamia-Athens, 35100 Lamia, Greece;2. University of Thessaly, Department of Agriculture, Crop Production & Rural Environment, Fytoko Street, N. Ionia, Magnesia 38446, Greece;1. Technological Educational Institute (T.E.I.) of Central Greece, 3rd Km Old National Road Lamia-Athens, Lamia 35100, Greece;2. Hasselt University, Campus Diepenbeek Agoralaan Gebouw D, BE3590 Diepenbeek, Belgium;3. Kielce University of Technology, al. Tysi?clecia Państwa Polskiego 7, 25-314 Kielce, Poland |
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Abstract: | In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning. |
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