JACIII Vol.10 No.4 pp. 472-476
doi: 10.20965/jaciii.2006.p0472


Hybrid Module of the IACVIRTUAL Project

Lourdes M. Brasil*, Jean C. C. Rojas**, Fernando M. de Azevedo***,
and Carlos W. D. de Almeida****

*Master’s Program in Knowledge and Information Technology Management/Biomedical Engineering Course, Catholic University of Brasília, Campus II, SGAN 916, Módulo B, Sala A 206 Asa Norte, Brasília DF 70790-160, Brazil

**Computer Science Course, Catholic University of Brasília, Campus II, SGAN 916, Módulo B, Sala A 206, Asa Norte, Brasília DF 70790-160, Brazil

***Biomedical Engineering Institute, Federal University of Santa Catarina, Trindade, Florianópolis, SC 88040-900, Brazil

****Technology Center, Department of Electronics and Systems, Federal University of Pernambuco, DES/CTG-UFPE, Rua Acadêmico Hélio Ramos s/n, sala 416, Cidade Universitária, Recife, PE 50740-530, Brazil

June 25, 2005
October 19, 2005
July 20, 2006
hybrid expert system, neural networks, knowledge acquisition, cardiology

This work represents the hybrid module of the IACVIRTUAL meta-environment. In this context we will basically approach the Hybrid Expert System (HES), which is composed by the Neural Networks Based Expert System (NNES) and by the Rule-Based Expert System (RBES). The HES is destined to support the decision of a clinical-surgical team, in the area of cardiology, in the definition of a therapeutic conduct in patients with coronary heart disease. The implementation process starts with the Knowledge Acquisition (KA), which comes from the analysis of a series of clinical parameters, which are used as input data for the NNES. This way, knowledge acquired during elicitation is converted in fuzzy rules. Through these rules, elicitated knowledge is mapped in AND/OR graphs, which then represent the starting structure of the NNES. Learning and optimization of the RBES are made through the Genetic-Backpropagation Based Learning Algorithm (GENBACK). This algorithm can, during the learning process, modify the weight of the connections, as well as the network structure. Knowledge abstracted from the RBES, being already refined, as well as trained and tested, is used to form the Knowledge Base of the RBES.

Cite this article as:
Lourdes M. Brasil, Jean C. C. Rojas, Fernando M. de Azevedo, and
and Carlos W. D. de Almeida, “Hybrid Module of the IACVIRTUAL Project,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.4, pp. 472-476, 2006.
Data files:
  1. [1] C. W. D. Almeida, et al., “Virtual Medical Office: A Medical and Educational Tool,” Proc. International Congress on Computational BioEngineering, Zaragoza, Spain, pp. 395-400, 2003.
  2. [2] C. W. D. de Almeida, R. A. Farias, L. M. Brasil, V. V. Coelho, R. Balaniuk, J. M. Lamas, A. E. M. Almeida, D. M. Bittar, and F. T. Ferreira, “WEB System for Medical Support using Virtual Reality,” In: Proceedings of the Second IASTED International Conference on BioMedical Engineering (BioMed 2004), pp. 409-412, Innsbruck, Austria, 2004.
  3. [3] J. C. C. Rojas, et al., “Sistema Especialista Híbrido: Uma Aplicação para Diagnóstico de Múltiplas Doenças,” Proc. VI Congresso Brasileiro de Redes Neurais, São Paulo, Brasil, 2003, CD-ROM (in Portuguese).
  4. [4] J. C. C. Rojas, “Sistema Especialista Híbrido de Apoio à Decisão de uma Equipe Clínico-Cirúrgica na Definição de Conduta Terapêutica em Pacientes Coronariopatas,” João Pessoa: Federal University of Paraíba, Brazil, 2003 (in Portuguese).
  5. [5] L. M. Brasil, “Proposta de Arquitetura para Sistema Especialista Híbrido e a Correspondente Metodologia de Aquisição do Conhecimento,” Florianópolis: Federal University of Santa Catarina, 1999 (in Portuguese).
  6. [6] L. M. Brasil, F. M. Azevedo, and J. M. Barreto, “A hybrid expert system for the diagnosis of epileptic crisis,” Special Issue in Artificial Intelligence in Medicine (AIM), 1(3): pp. 227-233, Amsterdam, Holland, 2000.
  7. [7] L. A. Zadeh, “Fuzzy Logic,” IEEE Computer, 21(4): No.4, 1988.
  8. [8] R. A. Aliev, B. Fazlollahi, and R. M. Vahidov, “Genetic Algorithm-Based Learning of Fuzzy Neural Networks, Part 1: Feed-Forward Fuzzy Neural Networks,” Fuzzy Sets and Systems, No.118, pp. 251-358, 2001.
  9. [9] S. H. Huang, and H. Xing, “Extract Intelligible and Concise Fuzzy Rules from Neural Networks,” Fuzzy Sets and Systems, No.132, pp. 233-243, 2002.
  10. [10] L. M. Brasil, and F. M. Azevedo, “Técnica de extração de regras para sistemas especialistas conexionistas,” Revista Brasileira de Engenharia Biomédica, Brazilian Journal of Biomedical Engineering, 19(1): pp. 7-17, Rio de Janeiro, Brasil, 2003 (in Portuguese).
  11. [11] P. G. Campos, et al., “Extraction Technique of Fuzzy Rules Applied to a Medical Expert System,” Proc. 2nd European Medical & Biological Engineering Conference, Vienna, Austria, pp. 740-741, 2002.

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Last updated on Mar. 01, 2021