TY - JOUR
T1 - Comparison some of kernel functions with support vector machines classifier for thalassemia dataset
AU - Wirasati, Ilsya
AU - Rustam, Zuherman
AU - Aurelia, Jane Eva
AU - Hartini, Sri
AU - Saragih, Glori Stephani
N1 - Funding Information:
This research used training data diverse from 10% to 90% and used = ? . ? for Gaussian RBF kernel and d=3 for polynomial kernel. The reason is, from the number of the experiment that is obtained, = ? . ? and d=3 has the best performance. This chosen = ? . ? is also supported by [16].
Funding Information:
This research full supported financially by University of Indonesia, with a PUTI SAINTEKES 2020 research grant scheme (ID number NKB-2408/UN2.RST/HKP.05.00/2020). And also, authors felt grateful and want to thank the Harapan Kita Children and Women's Hospital, Indonesia, for providing the thalassemia dataset.
Publisher Copyright:
© 2021, Institute of Advanced Engineering and Science. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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%.
AB - 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%.
KW - Classification
KW - Kernel function
KW - Machine learning
KW - Support vector machine
KW - Thalasssemia
UR - http://www.scopus.com/inward/record.url?scp=85107745733&partnerID=8YFLogxK
U2 - 10.11591/IJAI.V10.I2.PP430-437
DO - 10.11591/IJAI.V10.I2.PP430-437
M3 - Article
AN - SCOPUS:85107745733
VL - 10
SP - 430
EP - 437
JO - IAES International Journal of Artificial Intelligence
JF - IAES International Journal of Artificial Intelligence
SN - 2089-4872
IS - 2
ER -