Name
Title | ||
|---|---|---|
Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm |
Abstract | ||
|---|---|---|
In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system. The approach system has two stages. In the first stage, dimension of lung cancer dataset that has 57 features is reduced to 4 features using principal component analysis. In the second stage, artificial immune recognition system (AIRS) was our used classifier. We took the lung cancer dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) Machine Learning Database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.eswa.2006.10.011 | Expert Syst. Appl. |
Keywords | Field | DocType |
principal component analysis,classifier algorithm,lung cancer,used classifier,computer science,artificial immune system,medical diagnosis system,classification accuracy,classification application,airs,medical diagnosis,artificial immune recognition system,approach system,lung cancer dataset,principal component analysis (pca),machine learning | Artificial immune system,Recognition system,Pattern recognition,Computer science,Computer-aided,Artificial intelligence,Classifier (linguistics),Machine learning,Medical diagnosis,Principal component analysis,Information and Computer Science | Journal |
Volume | Issue | ISSN |
34 | 1 | Expert Systems With Applications |
Citations | PageRank | References |
13 | 0.96 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Polat | 1 | 1348 | 97.38 |
| Salih Güneş | 2 | 1267 | 78.53 |