CFP last date
20 December 2024
Reseach Article

Mosquito Borne Disease Incidence Prediction System using Fuzzy Weighted Associative Classification

by M. Kalpana Devi, M. Usha Rani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 13
Year of Publication: 2014
Authors: M. Kalpana Devi, M. Usha Rani
10.5120/15941-5171

M. Kalpana Devi, M. Usha Rani . Mosquito Borne Disease Incidence Prediction System using Fuzzy Weighted Associative Classification. International Journal of Computer Applications. 91, 13 ( April 2014), 15-21. DOI=10.5120/15941-5171

@article{ 10.5120/15941-5171,
author = { M. Kalpana Devi, M. Usha Rani },
title = { Mosquito Borne Disease Incidence Prediction System using Fuzzy Weighted Associative Classification },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 13 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number13/15941-5171/ },
doi = { 10.5120/15941-5171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:39.431283+05:30
%A M. Kalpana Devi
%A M. Usha Rani
%T Mosquito Borne Disease Incidence Prediction System using Fuzzy Weighted Associative Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 13
%P 15-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, applications attracted rampant attention in Epidemiology, Medical Entomology, Bio informatics, and Bio surveillance. Data mining applications is greatly useful to all stake holders in the healthcare industry. Associative Classification (AC) is a branch of data mining, a larger area of scientific study. To build a model for the purpose of prediction, AC is a suitable prediction technique, which integrates two data mining tasks, association rule mining and classification. The main aim of classification is the prediction of class labels, while association rule discovery describes relationship between items in a transactional database. Of late, Associative Classifier is having better accuracy as compared to that of traditional classifiers. Mosquito Borne Diseases that place a heavy burden on public health system on most of the tropical countries around the world. There is a need to develop prediction methods to augment existing control strategies. In this paper we use Fuzzy Weighted Associative Classifier to build an effective prediction model to predict mosquito borne disease incidence

References
  1. Priya Shetty, "Climate change and insect-borne disease:Facts and figures", Science and Development Network, 9September 2009.
  2. Zhou G, Minakawa N, Githeko AK, et al. Associationbetween climate variability and malaria epidemics in the East African highlands. Proc Natl Acad Sci U S A2004.
  3. Hales S, de Wet N, Maindonald J, et al. Potential effect ofpopulation and climate changes on global distribution of dengue fever: an empirical model. Lancet 2002.
  4. W. J. Tabachnick, "Challenges in predicting climate and environmental effects on vector-borne diseaseepisystems in a changing world", University ofFlorida, The Journal of Experimental Biology 213, 946-954© 2010.
  5. Environmental Factors in the View of Vectors & Vector borne Diseases, Environmental Information System,Center for bioinformatics and vector control.
  6. R. Agrawal, T. Imielinski, and A. Swami, 'MiningAssociation Rules between Sets of Items in LargeDatasets' Proc. ACM SIGMOD '93, pp. 207-216, 1993.
  7. R. Agrawal and R. Srikant, 'Fast Algorithms for Mining Association Rules', Proc. 20th Int'l Conf. Very Large Data Bases (VLDB '94), pp. 487-499, 1994.
  8. F. Bodon, 'A Survey on Frequent Itemset Mining', technical report, Budapest Univ. of Technology and Economics, 2006.
  9. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, 'Dynamic Itemset Counting and Implication Rules for Market Basket Data', Proc. ACM SIGMOD, 1997.
  10. J. S. Park, M. Chen, and P. S. Yu, 'An Effective Hash Based Algorithm for Mining Association Rules' Proc. ACM SIGMOD, 1995.
  11. B. Liu, W. Hsu, and Y. Ma. 'Integrating classification and association rule mining', In KDD'98, pp. 80-86, New York, NY, Aug. 1998.
  12. Delgado, M. , Marin, N. , Martin-Bautista, M. J. , Sanchez, D. and Vila, M. A. (2003),' Mining Fuzzy Association Rules: An Overview', In: Proceedings Of the BISC International Workshop on Soft Computing for Internet and Bioinformatics. Springer, Vol. 224, pp. 351-374.
  13. Kuok, C. M. , Fu, A. , Wong, M. H. : Mining Fuzzy Association Rules in Databases. SIGMOD Record 27(1), 41–46 (1998)
  14. Z. Chen and G. Chen, An approach to classification based on fuzzy association rules, in T. Li, Y. Xu, D. Ruan (Eds. ), Proc. Int. Conf. Intelligent Systems and Knowledge Engineering, 2007.
  15. G. D. Ramkumar, S. Ranka, and S. Tsur, "Weighted Association Rules: Model and Algorithm" Proc. ACM SIGKDD, 1998.
  16. C. H. Cai, A. W. C. Fu, C. H. Cheng, and W. W. Kwong, "Mining Association Rules with Weighted Items" Proc. IEEE Int'l Database Eng. and Applications Symp. (IDEAS '98), pp. 68-77, 1998.
  17. W. Wang, J. Yang, and P. S. Yu, "Efficient Mining of Weighted Association Rules (WAR)," Proc. ACM SIGKDD '00, pp. 270-274, 2000.
  18. F. Tao, F. Murtagh, and M. Farid, "Weighted Association Rule Mining Using Weighted Support and Significance Framework" Proc. ACM SIGKDD '03
  19. en. wikipedia. org/wiki/Tirupati_(city).
  20. Sunita Soni and O. P. Vyas 'Fuzzy Weighted Associative Classifier: APredictive Technique For Health Care DataMining', IJCSEI, Vol. 2, No. 1, February 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Epidemiology Medical Entomology Bioinformatics Bio surveillance Associative Classification Mosquito Borne Diseases Fuzzy Weighted Associative Classifier.