Performance Analysis of Clustering and Data Mining Methods on Medical and Clinical Data Through Rough Set Theory

Abstract

In this paper, we used two models for analysis, prediction, cost estimation, cost sensitivity and knowledge discovery rules for medical data by using rough set theory. In the first model,RGA and rule generation algorithms where used within which concepts of Generic Algorithms were utilized for the optimization in the analysis for the better performance and error reduction. Various attributes like scaling , itching, polygonal, age, erythematic etc were considered and and its count and percentage were calculated based on the model. Rules were distributed in various classes and its count was measured. Three methods were called for classification result generation viz. direct method, indirect method and meta-cost sensitive method and the result found were upto the mark. In the second method, Genetic, Exhaustive, Covering and LEM2algorithm were used and specificaaly they emphasized on PIMA data set for the analysis and rule generation.