Abstract
This paper discusses granular models of medical diagnostic rules which is an extension of rough set rule model. Medical diagnostic reasoning is characterized by three processes: focusing mechanism, differential diagnosis and detection of complications. First, focusing mechanism uses a set of symptoms which are always observed by almost all the cases of a candidate and if a case does not include any one of them, the candidate will be rejected. Second, from selected candidates, a set of symptoms which are highly observed in the cases are used for confirming the differential diagnosis. Finally, detection of complications is a set of symptoms whose occurrence of a candidate is very low but are very important for diagnosis of other diseases. These rule models can be easily described by an extension of rough set model: supporting sets of the first two sets of symptoms correspond to upper and lower approximations of a target concept. The final one is described by interrelations between a target concept and other concepts, which will be a new type of information granules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Implementation of detection of complications is not discussed here because it is derived after main two process, exclusive and inclusive reasoning. The way to deal with detection of complications is discussed in Sect. 5.
- 2.
This probabilistic rule is also a kind of rough modus ponens [3].
- 3.
However, deterministic rule induction model is still powerful in knowledge discovery context as shown in [8].
- 4.
The first term \(R=[a_i=v_j]\) may not be needed theoretically. However, since deriving conjunction in an exhaustive way is sometimes computationally expensive, here this constraint is imposed for computational efficiency.
References
Matsumura, Y., Matsunaga, T., Maeda, Y., Tsumoto, S., Matsumura, H., Kimura, M.: Consultation system for diagnosis of headache and facial pain: “rhinos”. In: Wada, E. (ed.) LP. Lecture Notes in Computer Science, vol. 221, pp. 287–298. Springer (1985)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z.: Rough modus ponens. In: Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 98. Paris (1998)
Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. Wiley, New York (1994)
Tsumoto, S.: Automated induction of medical expert system rules from clinical databases based on rough set theory. Inf. Sci. 112, 67–84 (1998)
Tsumoto, S.: Extraction of experts’ decision rules from clinical databases using rough set model. Intell. Data Anal. 2(3), 215–227 (1998)
Tsumoto, S.: Modelling medical diagnostic rules based on rough sets. In: Polkowski, L., Skowron, A. (eds.) Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science, vol. 1424, pp. 475–482. Springer, Heidelberg (1998)
Tsumoto, S.: Automated discovery of positive and negative knowledge in clinical databases based on rough set model (2000)
Tsumoto, S.: Extraction of hierarchical decision rules from clinical databases using rough sets. Inf. Sci. (2003)
Tsumoto, S.: Extraction of structure of medical diagnosis from clinical data. Fundam. Inform. 59(2–3), 271–285 (2004)
Tsumoto, S.: Rough sets and medical differential diagnosis. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems. Intelligent Systems Reference Library, vol. 42, pp. 605–621. Springer (2013)
Tsumoto, S., Tanaka, H.: Induction of probabilistic rules based on rough set theory. In: Jantke, K.P., Kobayashi, S., Tomita, E., Yokomori, T. (eds.) Algorithmic Learning Theory, 4th International Workshop, ALT 1993, Tokyo, Japan, 8-10 November 1993, Proceedings. Lecture Notes in Computer Science, vol. 744, pp. 410–423. Springer (1993). https://doi.org/10.1007/3-540-57370-4
Tsumoto, S., Tanaka, H.: Automated discovery of medical expert system rules from clinical databases based on rough sets. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 96, pp. 63–69. AAAI Press, Palo Alto (1996)
Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)
Acknowledgments
The author would like to thank past Professor Pawlak for all the comments on my research and his encouragement. Without his influence, one of the authors would neither have received Ph.D on computer science, nor become a professor of medical informatics. The author also would like to thank Professor Jerzy Grzymala-Busse, Andrezj Skowron, Roman Slowinski, Yiyu Yao, Guoyin Wang, Wojciech Ziarko for their insightful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tsumoto, S., Hirano, S. (2019). Granular Rules for Medical Diagnosis. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-21920-8_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21919-2
Online ISBN: 978-3-030-21920-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)