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

Mohammed M. Eissa, Mohammed Mahfouz Elmogy, Mohamed Hashem


The medical field is considered as one of the most significant research resources. It receives a great 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 data pre-processing, feature reduction, and generating rules based on the selected features for classification tasks. This paper proposes a framework of Rough Mereology for the classification of Hepatitis C Virus and Coronary Heart Disease medical data sets. The proposed system uses granular reflection mechanism based on rough inclusion to generate sets of granules at different radius. It selects the optimum radius based on accuracy to  induces set of rules that help medical experts to take treatment decisions. The experimental results show that the Rough based granular computing approach provides a complimentary and comprehensive tool for the analysis of medical datasets.

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