Multi Class Multi Label Based Fuzzy Associative Classifier with Genetic Rule Selection for Coronary Heart Disease Risk Level Prediction

R. Sumathi(1*), E. Kirubakaran(2), R. Krishnamoorthi(3)

(1) Professor, Department of Computer Science and Engineering, J.J.College of Engineering and Technology, Tiruchirapalli, Tamil Nadu, India, 620009., India
(2) Additional General Manager, BHEL, Tiruchirapalli, Tamil Nadu, India, 620014., India
(3) Professor, Department of Information Technology, Anna University, BIT Campus, Tiruchirapalli, Tamil Nadu, India, 620024., India
(*) Corresponding author


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Most of the associative classifiers are crisp in nature. They focus to develop two class classifier. Fuzzy based approach in associative classifier improves the accuracy of the classifier and also helps in developing classifier with more than two classes. According to the World Health Organization (WHO), coronary heart disease accounts for about 17 million (approximately 30%) deaths annually throughout the world. Previous works in the heart disease risk level prediction concentrated only two classes whether a person has a possibility of heart disease or not. In the field of medical, early detection of diseases helps in curative of the diseases. Hence, developing a classifier by considering more than two classes improves the medical decision support system. Our proposed clinical decision support system helps in identifying coronary heart disease at the early stage also. In this paper, we propose a classifier framework for various risk level prediction, which consists of three major components. The first component is fuzzification of a data set by using various membership function based on the attribute nature. Second is generating fuzzy rules with multi class multi label based associative classifier. Associative classifier aims to discover a small set of rules that forms an accurate classifier. We extended multi class multi label associative classifier to build classifier for more than two risk levels. Final component is a hybrid model for rule selection which is a combination of pre selecting the best rules with best confidence value and optimizing rule selection using genetic algorithm. Experimental results show the effectiveness of our proposed system
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Associative Classifier; Fuzzy Systems; Coronary Heart Disease; Genetic Algorithm

Full Text:

PDF


References


Jiawei Han M. K, (2006) Data Mining Concepts and Techniques,Morgan Kaufmann Publishers.

http://indiatoday.intoday.in/site/story/India%27s+no.1+killer:+Heart+ disease/1/9242.html

Abdul Nazeer. K. A & M. P. Sebastian,(2009) “Improving the accuracy and efficiency of the kmeans clustering algorithm”, in InternationalConference on Data Mining and KnowledgeEngineering (ICDMKE), Proceedings of the WorldCongress on Engineering, Vol 1, July, London, UK.

Fahim. A. M., A. M. Salem, F. A. Torkey and M. A. Ramadan,(2006) “An Efficient enhanced k-means clustering algorithm”, journal of Zhejiang University,Vol.10(7), 1626-1633.

Berkhin P (2002) Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA, http://citeseer.nj.nec.com/berkhin02survey.html

Ranjana Vyas, Lokesh Kumar Sharma, Om Prakash vyas, Simon Scheider Associtive Classifiers for Predictive analytics: Comparative Performance Study, second UKSIM European Symposium on Computer Modeling and Simulation 2008.

Pach. F, Gyenesei.A, and Abonyi.J, “Compact fuzzy association rule based classifier,” Expert Syst. Appl., vol. 34, no. 4, pp. 2406–2416,2008.

Yin.X and Han.J CPAR: Classification based on predictive association rule. In SDM 2003, San Francisco, CA, May 2003.

Keivan Kainmher, Mehmat Kaya, Jamal Jida (2011). “Fuzzy association rule mining framework and its application to effective fuzzy associative classification”. in John Wiley&Sons, Inc, WIREs Data Mining Knowl Discov 2011, vol. 00, pp.1-19.

Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. In Knowledge discovery and data mining (pp. 80–86).

Rakesh Agrawal and Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the Twentieth International Conference on Very Large Databases, pp. 487-499, Santiago, Chile, 1994.

Li.W, Han.J, and Pei.J. CMAR: Accurate and efficient classification based on multiple class association rules.In ICDM'01, pp. 369-376, San Jose, CA, Nov.2001.

Thabtah, F., Cowling, P. & Peng, Y. 2004 MMAC: A new multi-class, multi-label associative classification approach. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, pp. 217–224.

Thabtah, F., Cowling, P. & Peng, Y. 2005 MCAR: Multi-class classification based on association rule approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, pp. 1–7.

Fadi Thabtah, A review of associative classification mining, The Knowledge Engineering Review, Volume 22, Issue 1 (March 2007), Pages 37-65, 2007.

Zuoliang Chen, Guoqing Chen (2008), “Building an Associative Classifier based on Fuzzy Association Rules”. In International Journal of Computational Intelligence”, vol. 1, No. 3 pp. 262–273, August 2008.

Jes´us Alcal´a-Fdez, Rafael Alcal´a, and Francisco Herrera “A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning” IEEE Transactions on Fuzzy Systems, Vol. 19, No. 5 pp. 857-872, October 2011.

Hu.Y, Chen.R, and Tzeng.G, “Finding fuzzy classification rules using data mining techniques,” Pattern Recognit. Lett., vol. 24, no. 1–3, pp. 509– 519, 2003.

Ishibuchi.H, Yamamoto.T, and Nakashima.T, “Hybridization of fuzzy GBMLapproaches for pattern classification problems,” IEEE Trans. Syst., Man Cybern. B, Cybern., vol. 35, no. 2, pp. 359–365, Apr. 2005.

E. Mansoori, M. Zolghadri, and S. Katebi, “Sgerd: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE Trans.Fuzzy Syst., vol. 16, no. 4, pp. 1061–1071, Aug. 2008.

(2010) The UCI Repository website. [Online]. Available: http://archive.ics.uci.edu/

Thamarai Selvi, G., Duraiswamy, K., A technique to tumor detection from brain MRI images using FCM and neuro-fuzzy classifier, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1931-194.

Mary Gladence, L., Ravi, T., Mining the change of customer behavior in fuzzy time-interval sequential patterns with aid of Similarity Computation Index (SCI) and Genetic Algorithm (GA), (2013) International Review on Computers and Software (IRECOS), 8 (11), pp. 2552-256.

Quteishat, A.M., Optimized fuzzy Min-Max artificial neural network got cervical cancer application, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2967-297.

Karthikeyan, T., Balakrishnan, R., Microarray gene expression and multiclass cancer classification using improved PSO based evolutionary fuzzy ELM classifier with ICGA gene selection, (2013) International Review on Computers and Software (IRECOS), 8 (10), pp. 2532-253.

Vimal Kumar, D., Tamilarasi, A., Mining of optimized multi relational relation patterns for prediction system, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1356-136.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2019 Praise Worthy Prize