Rough Set Approach for Novel Decision Making in Medical Data for Rule Generation and Cost Sensitiveness

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)

Abstract

Data mining techniques can be applied in the area of Software Engineering for getting improved results. Medical data mining has great potential for exploring the hidden patterns in medical data and these patterns can be utilized for clinical diagnosis. Analysis of medical data is often concerned with the treatment of incomplete knowledge, with management of inconsistent pieces of information. In the present study, the theory of Rough set is applied to find dependence relationship among data, evaluate the importance of attributes, discover the patterns of data, learn common decision-making rules, reduce the redundancies and seek the minimum subset of attributes so as to attain satisfactory classification. It is concluded that the decision rules with and without reducts) generated by the rough set induction algorithms (Exhaustive, Covering, LEM2 and GA) not only provide new medical insight but also are useful for medical experts to analyze the problem effectively and find optimal cost.

Keywords

Genetic algorithm Induction algorithms Medical data mining Rough set Rule generation Reducts 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. of Computer Science & MathsBangalore UniversityBangaloreIndia
  2. 2.Dept. of MCA, Dayananda Sagar, College of EngineeringBharathiar UniversityCoimbatoreIndia

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