Knowledge Discovery in Medical Data by Using Rough Set Rule Induction Algorithms

In the performance of data mining and knowledge discovery activities, rough set theory has been regarded as a powerful, feasible and effective methodology. There is a need for analysis of medical data that deals with incomplete and inconsistent information with the tremendous manipulation at different levels. In this context, rough set rule induction algorithms are capable of generating decision rules which can potentially provide new medical insight and profound medical knowledge. By taking into consideration all the above aspects, the present investigation is carried out. The results clearly show that rough set approach is certainly a useful tool for medical applications. Relationships and patterns within this data could provide new medical knowledge. The genetic algorithms offer an attractive approach for solving the feature subset selection problem. The process of finding useful patterns or meaning in raw data has been called knowledge discovery in databases. The algorithms used for the present study are: Exhaustive search, Covering, LEM2 and Genetic algorithms. Rules are generated and improved in the case of the above mentioned four algorithms. Further, the generated rules are improved by applying the shortening ratio as 0.8. Some of the important results of the present investigation include maximum coverage of 100% is observed in the case of exhaustive and genetic algorithms.