Abstract
The applications of the rough set theory to identify the most relevant attributes and to induce decision rules from a medical data set are discussed in this paper. The real life medical data set concerns children with diabetes mellitus. Three methods are considered for identification of the most relevant attributes. The first method is based on the notion of reduct and its stability. The second method is based on particular attribute significance measured by relative decrease of positive region after its removal. The third method is inspired by the wrapper approach, where the classification accuracy is used for ranking attributes. The rough set approach additionally offers the set of decision rules. For the rough set based reduced data application of nearest neighbor algorithms is also investigated. The presented methods are general and one can apply all of them to different kinds of data sets.
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Stepaniuk, J. (1999). Rough set data mining of diabetes data. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095133
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DOI: https://doi.org/10.1007/BFb0095133
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