Application of fuzzy logic in assessing the risk of arterial occlusion in hypertensive patients
Abstract
The solutions to most of the problems a person faces in life are imprecise or non-deterministic. the inaccuracy results either from the lack of data collected from a particular problem, and this data is vague, inaccurate, or incomplete, which leads to a lack of information. Or from differences of opinion on a particular issue. The data on occlusive arterial disease was analyzed using the fuzzy system, and one of the most important conclusions reached in this research is that the basis of fuzzy logic is based on medical conclusions based on extensive statistical studies on facts about normal and pathological measurements in different environmental conditions. Its accuracy depends on the accuracy of the basic conclusions. Fuzzy logic can be considered an alternative to statistical studies if it is fed with direct clinical information
Downloads
References
Abonyi J. (2003):" Fuzzy model identification for control”, University of Veszprém, Hungary, http://www.fmt.vein.hu/softcomp/book
Abonyi, J., Szeifert, F., (2002) “Supervised fuzzy clustering for the identification of fuzzy classifiers” science direct
Albayrak, S. and Amasyali, F. (2003), “Fuzzy C-Means Clustering on Medical Diagnostic Systems”. International XII. Turkish Symposium on Artificial Intelligence and Neural Networks(TAINN).
American Heart Association.2009. "LDL and HDL Cholesterol: What's Bad and What's Good?" http://www.americanheart.org/presenter.jhtml?identifier=180.
Babuska, R., (2002), “Neuro-Fuzzy Methods for Modeling and Identification” , Springer-Verlag, Reprinted with permission.
Bhatt,T.V.,Kashipara, H.T.,(2009) “Fuzzy modeling and simulation for regulating the dose of anesthesia” ,IEEE
Bydon S. (2001): "Supervisory fuzzy controller for linear control system", MSc. department of Process control, University of Mining and Metallurgy, Poland, http://pcc.imir.agh.edu.pl/poz6/
Cartwright, H., (2008), “ Using Artificial Intelligence in Chemistry and Biology: A Practical Guide”, Taylor & Francis Group, LLC.
Chinwaraphat , S. , Sanpanich, A. , Pintavirooj, C. , Sangworasil, M., Tosranon, P.(2008) “A modified fuzzy clustering for white blood cell segmentation” , International Symposium on Biomedical Engineering
Colak.S., H., Isik.C, (2003), “fuzzy Oscillometric blood pressure classification”, IEEE.
Colledge,N.R.,Walker,B.R.,Ralston,S.H. (2010), “Davidson’s principles and practice”Churchill livingstone Elsevier
Dembele, D. and Kastner, P. (2003), “Fuzzy C-Means for Clustering Microarray Data”. Oxford Journals, Life Sciences, Bioinformatics, Vol. 19, No. 8, pp. 973-980.
Fischer, B. and Buhmann, J.M. November (2003), “Bagging for Path-Based Clustering”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 11., pp. 1411-1415.
G.Castellano, C.Castiello, A.M. Fanelli & M.Sidella , (2003), “Symbolic Fuzzy Clustering for Mining User Profiles” , Computer Science Department, University of Bari Via Orabona, Italy .
Halkidi, M. and Batistakis, Y. and Vazirgiannis, M. (2001 a), “on Clustering Validation Techniques”. Journal of Intelligent Information Systems, Vol. 17, pp. 107-145.
Halkidi, M., Batistakis, Y.and Vazirgiannis, M. (2001 b),”Clustering algorithms and validity measures” , Department of Informatics , Athens University of Economics & Business
Hsiao-Fan Wang (2003) , “Beyond Optimal Clustering” , NEWSLETTER , Operational Research Society of New Zealand, Inc. Auckland, New Zealand. http://www.utexas.edu/its/windows/database/datamodeling/dm/overview.html
Isik,C., Colak,S.(2003), “Fuzzy oscillometric blood pressure classification”, IEEE
Jain, A.K., Duin, P.W. and Mao, J.,(2000) , “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 22, NO. 1.
Jiawei Han , Micheline Kamber ,“data mining : concept and techniques” , (2000) , intelligent data base system research lab., school of computer science , simon fraser university , Canada.
Kei Chiu ,R., Chun Chang,Y.,(2010) “Fuzzy –Based health information monitoring and communication system”, IEEE
Malaviya Ashutosh and Peters Liliane, (2000)“ Fuzzy Handwritten Description Language: FOHDEL”, Journal of the Pattern Recognition Society, pp. 119-131,.
Marques, J.P., (2001), “Pattern Recognition : Concepts, Methods and Applications”, Springer.
Mitra, S., Pal, S., K.,(2005) “Fuzzy sets in pattern recognition and machine intelligence” science direct
Moraga, C., (2005), “Introduction to Fuzzy Logic”, ELEC. ENERG. vol. 18, No. 2
Peter,Crosta ,(2009), “what is hypertention? What causes hypertention? ” for Medical news today
Sivanandam, S. N., Sumathi, S. and Deepa , S. N. , (2007), “Introduction to Fuzzy Logic Using MATLAB”, Springer-Verlag Berlin Heidelberg, Berlin.
Timothy J. Ross, Jane M. Booker, W. Jerry Parkinson , (2002) ,”Fuzzy logic and probability applications : bridging the gap”, the American Statistical Association and the Society for Industrialand Applied Mathematics.
Torres, A.,Nieto, j.,(2006) “Fuzzy Logic in Medicine and Bioinformatics” Journal of Biomedicine and Biotechnology.
Tso, B. and Mather, P.M. (2001), “Classification Methods for Remotely Sensed Data”. London and New York.
Wang, X. Y., Whitwell, G. and Garibaldi, J. M. (2004), "The Application of a Simulated Annealing Fuzzy Clustering Algorithm for Cancer Diagnosis". Proceeding of the IEEE 4th International Conference on Intelligent System Design and Application, pp. 467-472.
Zhenyuan Wang., Rong Yang, Kwong-Sak Leung, (2010), “Nonlinear Integrals and Their Applications in Data Mining”, World Scientific Publishing Co. Pte. Ltd.
