Authors: M.A. Saleem Durai; D.P. Acharjya; A. Kannan; N.Ch. Sriman Narayana Iyengar
Addresses: School of Computing Sciences and Engineering, VIT University, Vellore 632014, Tamilnadu, India ' School of Computing Sciences and Engineering, VIT University, Vellore 632014, Tamilnadu, India ' Department of Information Science and Technology, Anna University, Chennai, India ' School of Computing Sciences and Engineering, VIT University, Vellore 632014, Tamilnadu, India
Abstract: Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.
Keywords: decision tables; indiscernibility; information systems; medical diagnosis; rough sets; knowledge mining; intelligent modelling; kidney cancer; cancer detection; data mining.
International Journal of Bioinformatics Research and Applications, 2012 Vol.8 No.5/6, pp.417 - 435
Published online: 10 Oct 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article