A Novel Fuzzy-Neural Based Medical Diagnosis System
In this paper, application of artificial neural networks
in typical disease diagnosis has been investigated. The real procedure
of medical diagnosis which usually is employed by physicians was
analyzed and converted to a machine implementable format. Then
after selecting some symptoms of eight different diseases, a data set
contains the information of a few hundreds cases was configured and
applied to a MLP neural network. The results of the experiments and
also the advantages of using a fuzzy approach were discussed as
well. Outcomes suggest the role of effective symptoms selection and
the advantages of data fuzzificaton on a neural networks-based
automatic medical diagnosis system.
[1] J.G. Wolff, "Medical Diagnosis as Pattern Recognition in a Framework
of Information Compression by Multiple Alignment, Unification and
Search", Elsevier Science, 2005.
[2] K.R. Sasikala, M. Petrou, "Fuzzy Classification with a GIS as an Aid to
Decision Making", 2004.
[3] N. Salim," Medical Diagnosis Using Neural Networks", 2004.
[4] T. Zrimec and I. Kononenko, "Feasibility analysis of machine learning
in medical diagnosis from aura images", University of Ljubljana, 2004.
[5] R. Fuller, Neural Fuzzy System, Abo Akademi University, 1995.
[6] S.Weigand, A.Huberman, and D.E.Rumelhart, Predicting the future: a
connectionist approach, International Journal of Neural Systems, 1990.
[7] A. Pomi, F. Olivera , BMC Medical Informatics and Decision Making,
Context-sensetive auto associative memories as expert systems in
medical diagnosis, BioMed Central, 2006.
[8] F.Steimann, K.P.Adlassnig, "Fuzzy Medical Diagnosis", Thesis, Wien
University.
[9] L A. Zadeh, Biological application of the Theory of Fuzzy sets and
System, Biocybernetics of the Central Nervous System, 1969.
[10] Wiser system, http://wiser.nlm.nih.gov/about.html .
[11] MedWeaver medical software system,
http://www.ovid.com/site/pdf/medweaverfactsheet.pdf.
[12] R. Schalkof, Artificial Neural Networks, Mc Graw Hill, 1994.
[1] J.G. Wolff, "Medical Diagnosis as Pattern Recognition in a Framework
of Information Compression by Multiple Alignment, Unification and
Search", Elsevier Science, 2005.
[2] K.R. Sasikala, M. Petrou, "Fuzzy Classification with a GIS as an Aid to
Decision Making", 2004.
[3] N. Salim," Medical Diagnosis Using Neural Networks", 2004.
[4] T. Zrimec and I. Kononenko, "Feasibility analysis of machine learning
in medical diagnosis from aura images", University of Ljubljana, 2004.
[5] R. Fuller, Neural Fuzzy System, Abo Akademi University, 1995.
[6] S.Weigand, A.Huberman, and D.E.Rumelhart, Predicting the future: a
connectionist approach, International Journal of Neural Systems, 1990.
[7] A. Pomi, F. Olivera , BMC Medical Informatics and Decision Making,
Context-sensetive auto associative memories as expert systems in
medical diagnosis, BioMed Central, 2006.
[8] F.Steimann, K.P.Adlassnig, "Fuzzy Medical Diagnosis", Thesis, Wien
University.
[9] L A. Zadeh, Biological application of the Theory of Fuzzy sets and
System, Biocybernetics of the Central Nervous System, 1969.
[10] Wiser system, http://wiser.nlm.nih.gov/about.html .
[11] MedWeaver medical software system,
http://www.ovid.com/site/pdf/medweaverfactsheet.pdf.
[12] R. Schalkof, Artificial Neural Networks, Mc Graw Hill, 1994.
@article{"International Journal of Information, Control and Computer Sciences:64032", author = "S. Moein and S. A. Monadjemi and P. Moallem", title = "A Novel Fuzzy-Neural Based Medical Diagnosis System", abstract = "In this paper, application of artificial neural networks
in typical disease diagnosis has been investigated. The real procedure
of medical diagnosis which usually is employed by physicians was
analyzed and converted to a machine implementable format. Then
after selecting some symptoms of eight different diseases, a data set
contains the information of a few hundreds cases was configured and
applied to a MLP neural network. The results of the experiments and
also the advantages of using a fuzzy approach were discussed as
well. Outcomes suggest the role of effective symptoms selection and
the advantages of data fuzzificaton on a neural networks-based
automatic medical diagnosis system.", keywords = "Artificial Neural Networks, Fuzzy Logic, MedicalDiagnosis, Symptoms, Fuzzification.", volume = "2", number = "1", pages = "226-5", }