Name
Abstract | ||
|---|---|---|
This paper discusses a correspondence between the core ideas of rough sets and medical differential diagnosis. Classically, a disease is defined as a set of symptoms, each of which gives the degree of confidence and coverage for the diagnosis. Diagnostic procedure mainly consists of the following three procedures: First, focusing mechanism (characterization) selects the candidates of differential diagnosis by using a set of symptoms. Secondly, additional set of symptoms make a differential diagnosis among the selected candidates. Finally, complications of other disease will be considered by symptoms which cannot be explained by the final candidates. This chapter mainly focuses on the first and second process and shows that thiese processes correponds to rules extracted by upper and lower approximation of supporting set of a given disease. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICSMC.2012.6377733 | Systems, Man, and Cybernetics |
Keywords | Field | DocType |
approximation theory,granular computing,inference mechanisms,medical diagnostic computing,probability,rough set theory,confidence degree,diagnosis coverage,focusing mechanism,information granule,lower approximation,medical differential diagnosis,rough set,upper approximation,Focusing mechanism,Granular computing,Rough sets,Rule Induction | Disease,Computer science,Rough set,Granular computing,Artificial intelligence,Rule induction,Probabilistic logic,Machine learning,Differential diagnosis | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-4673-1712-2 | 1 |
PageRank | References | Authors |
0.37 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shusaku Tsumoto | 1 | 1820 | 294.19 |
| Shoji Hirano | 2 | 11 | 4.58 |