Consultation system for diagnosis of headache and facial pain: `RHINOS”
Proceedings of the 4th conference on Logic programming '85
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Information Sciences: an International Journal
PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Modelling Medical Diagnostic Rules Based on Rough Sets
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Extraction of structure of medical diagnosis from clinical data
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
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This paper overviews the following two important issues on the correspondence between Pawlak’s rough set model and medical reasoning. The first main idea of rough sets is that a given concept can be approximated by partition-based knowledge as upper and lower approximation. Interestingly, thes approximations correspond to the focusing mechanism of differential medical diagnosis; upper approximation as selection of candidates and lower approximation as concluding a final diagnosis. The second idea of rough sets is that a concept, observations can be represented as partitions in a given data set, where rough sets provides a rule induction method from a given data. Thus, this model can be used to extract rule-based knowledge from medical databases. Especially, rule induction based on the focusing mechanism is obtained in a natural way.