Rough-Mereology Framework for Making Medical Treatment Decisions Based on Granular Computing

The medical field is considered as one of the most significant research resources. It receives a significant interest from researchers in the field of informatics and medical experts. It has a tremendous amount of data on various diseases and their symptoms that causes difficulty in diagnosing diseases. Therefore, several medical approaches based on knowledge discovery in the database have been proposed and developed. They include data mining techniques for cleaning, filtering, dimension reduction, and induction of rules. In this paper, a Rough-Mereology framework is proposed for classification of Hepatitis C Virus (HCV) and Coronary Heart Disease (CHD) medical datasets. The proposed system uses granular reflection mechanism based on rough inclusion to generate sets of granules at different radiuses. It selects the optimum radius based on accuracy to induce a set of rules that help medical experts to take Therapeutic Medical Decisions. During the experiments, the Rough based granular computing supplies a complimentary and comprehensive framework for the analysis of medical datasets.