Abstract
Soft computing techniques are widely used for the research in various fields nowadays. Artificial Neural Networks and various other soft computing techniques can be used for handling large data for diagnosis of particular disease. The increasing demand of Artificial Neural Network application for predicting the disease shows better performance in the field of medical decision making.The rough set theory proposed by Pawlak is one of the widely used research area nowadays. Rough set theory can be used for handling impression and uncertainty in data; therefore it can be used for medical diagnosis systems .This paper represents the use of artificial neural networks in predicting disease i.e. diagnosis, and use of Rough Set Theory for finding the indicators in diagnosis of Nephritis. The proposed technique involves training a Multi Layer Perceptron with a BP learning algorithm to recognize a pattern for the diagnosis and prediction of Nephritis and calculating significance factor using Rough Set Theory to find indicators. In this paper, a brief introduction about soft computing techniques used nowadays for diagnosis of disease is given. The other part introduces Nephritis and the proposed method for diagnosis of Nephritis.
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Raich, D., Kulkarni, P.S. (2014). Application of Artificial Neural Networks and Rough Set Theory for the Analysis of Various Medical Problems and Nephritis Disease Diagnosis. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_11
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DOI: https://doi.org/10.1007/978-3-319-02931-3_11
Publisher Name: Springer, Cham
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