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The impact of rough set approach on medical diagnosis for cost effective feature selection

Author:

Prof. Dr. P. K. Srimani, F.N.A.Sc., and Manjula Sanjay Koti

Subject Area:

Physical Sciences and Engineering

Abstract:

The medical diagnosis process can be interpreted as a decision making process, during which the physician includes the diagnosis of a new and unknown case from an available set of clinical data and from clinical experience which can be computerized. A method that enhances the performance of a model that is based on Rough set theory for feature selection and classification is proposed. For this purpose, the PIMA dataset is used. The proposed system provides the solution to a feature subset selection problem which is nothing but a task of identifying and analysing an optimal subset from a larger set of features. It is concluded that the method certainly helps in cost reduction associated with the diagnosis.

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