Economics at your fingertips  


Mihaela Gheorghe
Additional contact information
Mihaela Gheorghe: Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, Romania

Network Intelligence Studies, 2015, issue 5, 43-48

Abstract: Support vector machine represents an important tool for artificial neural networks techniques including classification and prediction. It offers a solution for a wide range of different issues in which cases the traditional optimization algorithms and methods cannot be applied directly due to different constraints, including memory restrictions, hidden relationships between variables, very high volume of computations that needs to be handled. One of these issues relates to medical diagnosis, a subset of the medical field. In this paper, the SVM learning algorithm is tested on a diabetes dataset and the results obtained for training with different kernel functions are presented and analyzed in order to determine a good approach from a telemedicine perspective.

Keywords: Support vector machine; Medical diagnosis; Classification; Artificial neural network; Kernel function (search for similar items in EconPapers)
JEL-codes: C45 I10 (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Network Intelligence Studies is currently edited by Fundația Română pentru Inteligența Afacerii

More articles in Network Intelligence Studies from Fundația Română pentru Inteligența Afacerii, Editorial Department
Series data maintained by Serghie Dan ().

Page updated 2017-10-31
Handle: RePEc:cmj:networ:y:2015:i:5:p:43-48