Optimistic Multi-Granulation Rough set based Classification for Medical Diagnosis

Medical analysis has been approached by several machine learning methods for many years. Pattern recognition algorithms are capable of improving the quality of prediction, early diagnosis of diseases and disease classification. The classification complications in medical area are solved based on the outcome of medical analysis or report of medical treatment by the medical specialist. This research focuses on applying Rough set based data mining techniques for medical data to discover locally frequent diseases. This work applies Optimistic Multi-granulation rough set model (OMGRS) for medical data classification. Multi-granulation rough set provides efficient results than single granulation rough set model and soft rough set based classifier model. The results of applying the OMGRS methodology to medical diagnosis based upon selected information. The performance of the proposed optimistic multi granulation Rough set based classification is compared with other rough set based (RS), K-th Nearest Neighbor (KNN) and Back propagation neural network (BPN) approaches using various classification Measures. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).