Performance of Fuzzy Rough Sets and Fuzzy Evolutionary Classifiers using Medical Databases
S. Poongothai1, C. Dharuman2, P. Venkatesan3

1S. Poongothai, Asst. Professor, Department of Mathematics, SRM University, Ramapuram Campus, Chennai, India
2C. Dharuman, Professor, Department of Mathematics, SRM University, Ramapuram Campus, Chennai, India
3P. Venkatesan, Faculty of Research, Sri Ramachandra University, Chennai, India

Manuscript received on 02 August 2019 | Revised Manuscript received on 05 August 2019 | Manuscript published on 30 August 2019 | PP: 4301-4304 | Volume-8 Issue-10, August 2019 | Retrieval Number: J10660881019/19©BEIESP | DOI: 10.35940/ijitee.J1066.0881019
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Abstract: As the technology improving, the problems of mankind, regarding health issues also increasing day by day. Nowadays high dimensionality data are available for various health problems which is very difficult to handle manually. The aim of this paper is to construct algorithms for extracting the relevant information from the large amount of data and classifying using various hybrid techniques like Fuzzy-Rough set and Fuzzy Evolutionary Algorithms. The efficiency of Fuzzy classifiers has been improved by hybridization method. This paper proposes a comparison of fuzzy hybrid techniques like Fuzzy Rough set and Fuzzy EA for the diagnosis of Hepatitis taken from UCI repository. The results of comparison and classification shows that the proposed technique performs better than other normal methods.
Keywords: Fuzzy Logic, Rough Sets, Evolutionary Algorithms, Hybrid Techniques

Scope of the Article: Classification