Abstract
Rough sets, surrounded by two approximation sets filled with sure and possible members constitute perfect mathematical tools of the classification of some objects. In this work we adopt the rough technique to verify diagnostic decisions concerning a sample of patients whose symptoms are typical of a considered diagnosis. The objective is to extract the patients who surely suffer from the diagnosis, to indicate the patients who are free from it, and even to make decisions in undefined diagnostic cases. By applying selected logical decision rules, we also discuss a possibility of reducing of symptom sets to their minimal collections preserving the previous results in order to minimize a number of numerical calculations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bazan, J., Nguyen, H.S., Szczuka, M.: A View on Rough Set Concept Approximations. Fundamenta Informaticae 59, 107–118 (2004)
Jensen, R., Shen, Q.: Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering 16(12), 1457–1471 (2004)
Lin, T.Y., Chen, R.: Finding Reducts in Very Large Databases. In: Proc. Joint Conf. Information Science Research, pp. 350–362 (1997)
Pal, S.K., Skowron, A. (eds.): Rough Fuzzy Hybridization: New trends in Decision Making. Springer, Singapore (1999)
Pal, S.K., Mitra, P.: Multi-layer Perception, Fuzzy Sets and Classification. IEEE Trans. Neural Networks 3, 683–697 (1992)
Pal, S.K., Mitra, P.: Case Generation Using Rough Sets with Fuzzy Representation. IEEE Transactions on Knowledge and Data Engineering 16(3), 292–300 (2004)
Pawlak, Z.: Rough Sets. Int. J. Computer and Information Science 11, 341–356 (1982)
Pawlak, Z.: On Rough Sets. Bulletin of the EATCS 24, 94–108 (1984)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)
Pawlak, Z.: Vagueness – a Rough Set View. Structures in Logic and Computer Science, 106–117 (1997)
Pawlak, Z.: Decision Networks. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 1–7. Springer, Heidelberg (2004)
Rakus, E.: Fuzzy Set Theory Assisting Medical Diagnosis and Appreciation of Drug Effectiveness. Doctor’s dissertation, Medical Academy of Łódź (1991) (in Polish)
Rakus-Andersson, E.: Fuzzy and Rough Techniques in Medical Diagnosis and Medication. Springer, Heidelberg (2007)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support. In: Skowron, A. (ed.) Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic, Dordrecht (1992)
Yao, J.T., Yao, Y.Y.: Induction of Classification Rules by Granular Computing. In: Proc. of the Third International Conference on Rough Sets and Current Trends in Computing (TSCTC 2002), pp. 331–338. Springer, London (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Rakus-Andersson, E. (2009). Rough Set Theory in the Classification of Diagnoses. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds) Computers in Medical Activity. Advances in Intelligent and Soft Computing, vol 65. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04462-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-04462-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04461-8
Online ISBN: 978-3-642-04462-5
eBook Packages: EngineeringEngineering (R0)