Name
Abstract | ||
|---|---|---|
AbstractA Case-Based Reasoning CBR system for medical diagnosis mimics the way doctors make a diagnosis. Given a new case, its accuracy in practice depends on successful retrieval of similar cases. CBR systems have had some success in dealing with simple diseases because of the robustness of their case base. However, their diagnostic accuracy suffers when dealing with complex diseases particularly those that involve multiple domains in medicine. An example of such a condition is Premenstrual syndrome PMS as it falls under both gynaecology and psychiatry. To address this issue, the paper proposes a CBR-based expert system that uses the K-nearest neighbour KNN algorithm to search k similar cases based on the Euclidean distance measure. The novelty of the system is in the design of a flexible auto-set tolerance T, which serves as a threshold to extract cases for which similarities are greater than the assigned value of T. A prototype software tool with a menu-driven Graphical User Interface GUI has been developed for case input, analysis of results, and case adaptation within the system. Finally, the performance of the tool has been checked on a set of real-world PMS cases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1111/j.1468-0394.2012.00618.x | Periodicals |
Keywords | Field | DocType |
CBR system,PMS,Master Case base (MCB),menu-driven approach (MDA),auto-set tolerance (T),KNN | k-nearest neighbors algorithm,Data mining,Computer science,Expert system,Robustness (computer science),Euclidean distance measure,Graphical user interface,Artificial intelligence,Novelty,Case-based reasoning,Medical diagnosis,Machine learning | Journal |
Volume | Issue | ISSN |
30 | 1 | 0266-4720 |
Citations | PageRank | References |
9 | 0.72 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhagata Chattopadhyay | 1 | 334 | 21.66 |
| Suvendu Banerjee | 2 | 9 | 0.72 |
| Fethi Rabhi | 3 | 427 | 50.68 |
| Rajendra Acharya U | 4 | 4666 | 296.34 |