CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Generating Rules for Advanced Fuzzy Resolution Mechanism to Diagnosis Heart Disease

by A. V Senthil Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 11
Year of Publication: 2013
Authors: A. V Senthil Kumar
10.5120/13436-0477

A. V Senthil Kumar . Generating Rules for Advanced Fuzzy Resolution Mechanism to Diagnosis Heart Disease. International Journal of Computer Applications. 77, 11 ( September 2013), 6-12. DOI=10.5120/13436-0477

@article{ 10.5120/13436-0477,
author = { A. V Senthil Kumar },
title = { Generating Rules for Advanced Fuzzy Resolution Mechanism to Diagnosis Heart Disease },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 11 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number11/13436-0477/ },
doi = { 10.5120/13436-0477 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:59.080390+05:30
%A A. V Senthil Kumar
%T Generating Rules for Advanced Fuzzy Resolution Mechanism to Diagnosis Heart Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 11
%P 6-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy logic plays an important role in the field of medicine. Many diseases are diagnosed using fuzzy logic. Heart disease is the number one killer to the human community throughout the world. This study was conducted to diagnosis the heart disease among the patients. The components of this study are Fuzzification, Generating rules for Advanced Fuzzy Resolution Mechanism and defuzzification. Crisp values are transformed into fuzzy values through the fuzzification. Generating rules for Advanced Fuzzy Resolution Mechanism has five layers, each layer has its own nodes. In layer 1 rule are generated with the data to frame the new rules and output parameter are predicted. The proposed algorithm is tested with Cleveland heart disease dataset. Generating rules for Advanced Fuzzy Resolution Mechanism was developed using MATLAB Fuzzy Logic Tool Box. Transformation of fuzzy set into crisp values is called Defuzzification. The proposed algorithm can work more efficiently to diagnosis heart disease and also compared with earlier method using accuracy as metrics.

References
  1. A. V. Senthil Kumar, "Adaptive Neuro-Fuzzy Inference System for Heart Disease Diagnosis",International Conference on Information System, Computer Engineering & Application (ICISCEA 2011), Singapore, pp. 91-99,2011.
  2. A. V. Senthil Kumar, " Diagnosis of Heart Disease using Fuzzy Resolution Mechanism" , Journal of Artificial Intelligence 5(1), ISSN 1994-5450, Asian Network for Scientific Information, pp. 47-55, 2012.
  3. A. V. Senthil Kumar, "Adaptive Neuro-Fuzzy Inference System based on substractive clustering to diagnosis the heart disease" , International Journal of Advances in Knowledge Engineering & Computer Science, Vol. 1, Issue. 2, 12 , pp. 01-11. June – 20
  4. Ali Sadighi, and Won-jong Kim, "Adaptive-Neuro-Fuzzy-Based Sensorless Control of a Smart-Material Actuator", IEEE/ASME Transactions on Mechatronics, Vol. 16, No. 2 pp. 374,2011.
  5. B. Ster and A. Dobnikar, . "Neural networks in medical diagnosis: Comparison with other methods". In: A. Bulsari et al. , editor, Proceedings of the International Conference EANN '96, pp. 427-430,1996. 1
  6. Detlef Nauck, Ulrike Nauck and Rudolf Kruse, "Generating Classification Rules with the Neuro-Fuzzy System NEFCLASS", Proc. Biennial conf. of the North American fuzzy Information Processing Society, pp. 1-6,1996.
  7. Duch W, Adamczak R, Gr?bczewski K, ?al G, " A hybrid method for extraction of logical rules from data", Second Polish Conference on Theory and Applications of Artificial Intelligence, pp. 61-82,1998.
  8. Duch W, Grudzinski K and Diercksen G. H. F ," Minimal distance neural methods", World Congress of Computational Intelligence, Anchorage, Alaska, IEEE IJCNN'98 Proceedings, pp. 1299-1304,1998.
  9. Duch W, Grudzi?ski K, "A framework for similarity-based methods", Second Polish Conference on Theory and Applications of Artificial Intelligence, pp. 33-60, 1998.
  10. Harsh Bhasin and Supreet Singh, "GA-Correlation Based Rule Generation for Expert Systems", Harsh Bhasin et al, International Journal of Computer Science and Information Technologies, Vol. 3 (2), pp. 3733-3736, 2012.
  11. Humar Kahramanli, Novruz Allahverdi,"Design of a hybrid system for the Diabetes and heart diseases", Expert system with applications 35, pp. 82-89, 2008.
  12. J. -S. R. Jang,"ANFIS: adaptive network-based fuzzy inference system", IEEE Trans. Sys. Man. Cybern. , vol. 23, pp. 665–685,1993.
  13. Jankowski N, Kadirkamanathan V, "Statistical Control of RBF-like Networks for Classification",7th International Conference on Artificial Neural Networks, Lausanne, Switzerland, pp. 385-390,1997.
  14. Li-Xin Wang, "Generating Fuzzy Rules by Learning from Examples", IEEE Transactions on Systems, Man and Cybernetics, Vol. 22, No. 6, pp. 1414-1427,2006.
  15. Loo, C. K. , Rao, M. V. C. ,"Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP", IEEE Transactions on Knowledge and Data Engineering, Volume: 17 Issue:11 pp. 1589 – 1593, 2005.
  16. M. Forouzanfar, H. R. Dajani, V. Z. Groza, M. Bolic, and S. Rajan," Adaptive neuro-fuzzy inference system for oscillometric blood pressure estimation" in Proc. IEEE Int. Workshop MeMeA, pp. 125–129, 2010.
  17. Marcelo de Carvalho Alves, Edson Ampélio Pozza , João de Cássia do Bonfim Costa, Luiz Gonsaga de Carvalho , Luciana Sanches Alves ,"Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust", Environmental Modelling & Software 26 pp. 1089-1096, 2011.
  18. Mehdi Fasanghari, Gholam Ali Montazer,"Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation", Expert Systems with Applications 37 pp. 6138–6147, 2010.
  19. Min Liu, Mingyu dong and Cheng Wu," A New ANFIS for Parameter Prediction with Numeric and Categorical Inputs", IEEE Transaction on Automation Science and Engineering Vol. & No. 3. pp. 645-653, 2010.
  20. Min-You Chen and D. A. Linkens, "Rule-base self-generation and simplification for data-driven fuzzy models", Fuzzy Sets and Systems Vol. 142, pp. 243-265, 2004.
  21. Mukhopadhyay, S. , Tang, C. , Huang, J. , Yu, M. , & Palakal, M, "A comparative study of genetic sequence classification algorithms, neural networks for signal processing", In Proceedings of the 2002 12th IEEE workshop, pp. 57–66. 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Generating rules for Advanced Fuzzy Resolution Mechanism Rules fuzzy predicted value Heart disease

Powered by PhDFocusTM