Abstract
A MES is developed which simulates the methodology of a medical practitioner’s way of detecting Cardiovascular diseases. The challenge of missing data has been solved by most frequent value imputation method. The issue of continuous attributed data is solved by entropy-based discretization method. The model seems to predict the presence or absence of heart disease from minimal attribute set thus minimizing redundancy. Reducts has been extracted from the features by considering quality of approximation. Learning by Example Module, Version 2 (LEM2) algorithm based on Rough Set has been used for rule induction and generation. Two Rough Set classifiers are designed, namely RSC-1 and RSC-2. Each classifier is fed with all features and then with reducts separately as inputs. A comparative study is performed between the classifiers, and result shows that RSC-1 whose input is locally discretized reduct performed better with an accuracy of \(84.46 \pm 5.24\) percentage.
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Acknowledgements
We would like to thank Dr. Szymon Wilk [Asst. Professor, Poznan University of Technology] who provided insight as well as expertise, that greatly assisted the Study.
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Chaudhuri, S.B., Rahman, M. (2018). Design of a Medical Expert System (MES) Based on Rough Set Theory for Detection of Cardiovascular Diseases. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-10-6872-0_30
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