Name
Abstract | ||
|---|---|---|
Medical diagnosis is a very complex task in the case where information suffer from various imperfections. That's why doctors rely on their knowledge and previous experiences to take the adequate decision. In this context, the case based reasoning (CBR) paradigm aims to resolve current problems basing on previous knowledge. Using ontologies to store and represent the background knowledge may notably enhance and improve the CBR semantic effectiveness. This paper proposes a possibilistic ontology based CBR approach in order to perform a possibilistic semantic retrieval algorithm that handles ambiguity and uncertainty problems. The approach is implemented and tested on the mammographic domain. The target ontology is instantiated with 113 real cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-65930-5_29 | Lecture Notes in Business Information Processing |
Keywords | DocType | Volume |
Case Based Reasoning,Knowledge management,Possibilistic ontology,Breast cancer diagnosis,Case retrieval | Conference | 299.0 |
ISSN | Citations | PageRank |
1865-1348 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yosra Ben Salem | 1 | 0 | 0.34 |
| Idoudi, R. | 2 | 1 | 1.71 |
| Karim Saheb Ettabaâ | 3 | 2 | 1.59 |
| Kamel Hamrouni | 4 | 41 | 21.73 |
| Basel Solaiman | 5 | 127 | 35.05 |