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In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification. [ABSTRACT FROM AUTHOR]

Medical datasets consume enormous amount of information about the patients, diseases and the physicians. Diseases diagnosis required many expensive tests to predict the diseases. Cost of disease prediction and diagnosis can be reduced by applying machine learning and data mining methods. Disease prediction and decision making plays asignificant role in medical diagnosis. In this study, a novel neighborhood rough set classification approach is presented to deal with medical datasets. Five benchmarked medical datasets have been used in this research work for studying the impact of proposed work in decision making.Experimental resultof the proposed classification algorithm is compared with other existing approaches such as rough set, K th –nearest neighbor, support vector machine, Back propagation algorithm and multilayer perceptron to conclude that the proposed approach is cheaper way for disease prediction and decision making. The performance of classification algorithms measured based on various classification accuracy measures. [ABSTRACT FROM AUTHOR]