Abstract
Clinical databases have been accumulated with large amounts of data due to the advances in the medical field. A medical dataset usually contains objects/records of patients that include a set of symptoms that a patient experiences. Medical data will have uncertainty due to the reason that a patient suffering from a specific illness cannot be completely determined by one or more symptoms; a certain set of symptoms can only indicate that there is a probability of a particular illness. Analysis of such medical data could reveal new insights that would definitely help in efficient diagnosis and also in drug discovery. This paper proposes a fuzzy-rough set based rule induction classifier to analyze medical data. In addition, we have presented a rough set based data preprocessing approach.
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References
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann
Hong J (2011) An improved prediction model based on fuzzy-rough set neural network. Int J Comput Theor Eng 3(1):1793–8
Little RJA, Rubin DB (1987) Statistical analysis with missing data. Wiley, New York
Maimon O, Rokach L (2005) Decomposition methodology for knowledge discovery and data mining: theory and applications. World Scientific, Singapore
Rokach L, Maimon O, Arad O (2005) Improving supervised learning by sample decomposition. Int J Comput Intell Appl 5(1):37–54
Dubois D, Prade H (1992) Putting rough sets and fuzzy sets together. In: Slowinski R (ed) Intelligent decision support: handbook of applications and advances of the rough sets theory. Kluwer Academic Publishers, Boston, pp 203–222
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishing, Dordrecht
Polkowski L (2002) Rough sets: mathematical foundations. Advances in soft computing. Physica Verlag, Heidelberg, Germany
Zhao J, Zhang Z (2011) Fuzzy-rough neural network and its application to feature selection. Int J Fuzzy Syst 13(4)
Mac Parthalain N, Jensen R (2010) Measures for unsupervised fuzzy-rough feature selection. Int J Hybrid Intell Syst 7:1C11
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Sujatha, M., Lavanya Devi, G., Naresh, N., Srinivasa Rao, K. (2016). An Efficient Classification Model Based on Ensemble of Fuzzy-Rough Classifier for Analysis of Medical Data. In: Bhramaramba, R., Sekhar, A. (eds) Application of Computational Intelligence to Biology. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-0391-2_3
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DOI: https://doi.org/10.1007/978-981-10-0391-2_3
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