A SUPPORT VECTOR MACHINE APPROACH FOR DEVELOPING TELEMEDICINE SOLUTIONS: MEDICAL DIAGNOSIS

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