AI Article Synopsis
- Medical diagnosis processes can vary significantly in how they address complications like the importance of symptoms, the variety of symptom patterns, and interrelations between diseases.
- The rough set approach offers advantages by being able to handle various data types without the assumptions made in other modeling methods, focusing on pattern recognition through logical rules instead of fitting mathematical functions.
- This paper applies rough set theory as a data mining tool on historical data from 25 research hospitals to identify useful patterns for diagnosing kidney cancer, demonstrating the practical effectiveness of this method.
Article 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.
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| http://dx.doi.org/10.1504/IJBRA.2012.049625 | DOI Listing |
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