Name
Abstract | ||
|---|---|---|
Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.01.042 | Neurocomputing |
Keywords | Field | DocType |
Support Vector Regression,Multi-Objective Genetic Algorithm,Disease diagnosis,Machine learning | Kernel (linear algebra),Data mining,Evolutionary algorithm,Regression,Categorical variable,Computer science,Support vector machine,Sorting,Artificial intelligence,Variables,Machine learning,Medical diagnosis | Journal |
Volume | ISSN | Citations |
136 | 0925-2312 | 4 |
PageRank | References | Authors |
0.42 | 35 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Hossein Zangooei | 1 | 74 | 4.55 |
| Jafar Habibi | 2 | 387 | 45.06 |
| Roohallah Alizadehsani | 3 | 111 | 9.60 |