The week's pick
Random Articles
Reseach Article
Rough Set Approach for Generation of Classification Rules for Hepatitis
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 2 - Number 2 |
| Year of Publication: 2015 |
| Authors: Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota |
10.5120/cae-1676
|
Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota . Rough Set Approach for Generation of Classification Rules for Hepatitis. Communications on Applied Electronics. 2, 2 ( June 2015), 22-27. DOI=10.5120/cae-1676
Abstract
In the current age research in the field of medical science has been increased to a significant height but there are several new virus which cannot be detect by the usual medical test , for example some common disease like malaria ,dengue, hepatitis , jaundice needs of very meticulous medical analysis because all the above said dieses has very common symptoms which needs of strong analysis to determine the exact dieses. Maximum number of medical test which are conducted to determine the dieses mostly based upon doctor's guess which are not only expensive and but also give inaccurate pathological result. In this paper we emphasized more on symptom rather than pathological test . From the large domain we consider the disease hepatitis for our purpose . Every year millions of people died from hepatitis due to improper diagnosis . We develop an algorithm using rough set concept to counter hepatitis. We classified the entire paper in to three basic section 1st section about literature review the 2nd and 3rd section deals with the Experiment, Findings, and Statistical Validation.
References
- Cios, K. , W. Pedrycz and R. Swiniarski (1998). Data Mining Methods for Knowledge Discovery. Kluwer Academic
- Wolf, S. , H. Oliver, S. Herbert and M. Michael (2000). Intelligent data mining for medical quality management
- Se-Ho, Ch. , and P. Rockett (2002). The training of neural classifiers with condensed datasets. SMCB, 32(2), 202–206. ,
- Setiono, R. (2000). Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine, 18(3), 205–219
- Cheeseman, P. , and J. Stutz (1996). Bayesian classification (AutoClass): theory and results. In U. M. Fayyad
- Grzymala–Busse, J. , Z. Pawlak, R. Slowinski and W. Ziarko (1999). Rough sets. Communications of the ACM
- Hassanien, A. E. (2003). Classification and feature selection of breast cancer data based on decision tree algorithm
- Parido, A. , and P. Bonelli (1993). A new approach to fuzzy classifier systems. In Proceedings of the FifthInternational Conference on Genetic Algorithms. pp. 223–230
- Lin, T. Y. , and N. Cercone (1997). Rough Sets and Data Mining. Kluwer Academic Publishers. Ning, S. , H. Xiaohua, W. Ziarko and N. Cercone (1994). A generalized rough sets model. In Proceedings ofthe 3rd Pacific Rim International Conference on Artificial Intelligence, Vol. 431. Beijing, China. Int. Acad. Publishers. pp. 437–443.
- Pawlak, Z. (1991). Rough Sets-Theoretical Aspect of Reasoning about Data. Kluwer Academic Publishers. Pawlak, Z. , J. Grzymala–Busse, R. Slowinski, W. Ziarko (1995). Rough sets. Communications of the ACM.
- Renu Vashist Prof M. L Garg Rule Generation based on Reduct and Core :A rough set approach International Journal of Computer Application(0975-887) Vol 29 September -2011 Page 1-4
Index Terms
Keywords