Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

Ilsya Wirasati, Zuherman Rustam, Jane Eva Aurelia, Sri Hartini, Glori Stephani Saragih

Research output: Contribution to journalArticlepeer-review

Abstract

In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.

Original languageEnglish
Pages (from-to)430-437
Number of pages8
JournalIAES International Journal of Artificial Intelligence
Volume10
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Classification
  • Kernel function
  • Machine learning
  • Support vector machine
  • Thalasssemia

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