Abstract
This chapter dicusses formalization of medical diagnosis from the viewpoint of rule reasoning based on rough sets. Medical diagnosis consists of the following three procedures. First, screening process selects the diagnostic candidates, where rules from upper approximations are used. Then, from the selected candidates, differential diagnosis is evoked, in which rules from lower approximations are used. Finally, consistency of the diagnosis will be checked with all the inputs: inconsistent symptoms suggest the existence of complications of other diseases. The final process can be viewed as complex relations between rules. The proposed framework successfully formalizes the representation of three types of reasoning styles.
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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.
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Acknowledgements
The author would like to thank past Professor Pawlak for all the comments on my research and his encouragement. Without his influence, the author 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. This research is supported by Grant-in-Aid for Scientific Research (B) 15H2750 from Japan Society for the Promotion of Science (JSPS).
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Tsumoto, S. (2017). Medical Diagnosis: Rough Set View. In: Wang, G., Skowron, A., Yao, Y., Ślęzak, D., Polkowski, L. (eds) Thriving Rough Sets. Studies in Computational Intelligence, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-54966-8_7
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