A Soft Computing Method for Typical Computation in Medical Data
Janmejay pant1, Manoj Kumar Singh2, Amit Juyal3, Himanshu Pant4, Chetan Pandey5
1Janmejay Pant*, Dept. of Computer Science, Graphic Era Hill University, Bhimtal, India.
2Manoj Kumar Singh, Dept. of Computer Science, Graphic Era Hill University, Bhimtal, India.
3Amit Juyal , Dept. of Computer Science, Graphic Era Hill University, Dehradun, India.
4Himanshu Pant, Dept. of Computer Science, Graphic Era Hill University, Bhimtal, India.
5Chetan Pandey, , Dept. of Computer Science, Graphic Era Hill University, Dehradun, India.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 26, 2020. | Manuscript published on February 10, 2020. | PP: 397-402 | Volume-9 Issue-4, February 2020. | Retrieval Number: D9078019420/2020©BEIESP | DOI: 10.35940/ijitee.D9078.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are several methods of Soft Computing for analyzing the complex data for making decisions and predictions. Rough Set Theory (RST) is one of the best and relatively new intelligent techniques used in different research area for making predictions. RST is used to discover the patterns of data, handle all the redundant objects and attributes. RST is majorly used for extraction the rules from the given data. In this paper, we will use a medical data set example of cancer for retrieving the rule which is useful to make prediction for the unknown class.
Keywords: Rough Set Theory, Information System, Decision Class, Indiscernibility, Discernibility Matrix, Equivalence Class, Rules
Scope of the Article: Information Centric Networking