TY - JOUR
T1 - Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification
AU - Saragih, Glori Stephani
AU - Hartini, Sri
AU - Rustam, Zuherman
N1 - Funding Information:
The authors were grateful to the Tangerang Hospital and Mitra Keluarga Kelapa Gading Hospital for their kindness in providing the hepatitis datasets, and all the reviewers included in the improvement of this article. This research was financially supported by Universitas Indonesia, with PUTI SAINTEKES 2020 research grant scheme (ID number NKB-2380/UN2.RST/HKP.05.00/2020).
Publisher Copyright:
© 2021, Institute of Advanced Engineering and Science. All rights reserved.
PY - 2021
Y1 - 2021
N2 - This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.
AB - This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.
KW - Classification
KW - Fuzzy kernel k-medoids
KW - Hepatitis
KW - Kernel function
KW - Polynomial kernel
KW - Radial basis function
UR - http://www.scopus.com/inward/record.url?scp=85103081197&partnerID=8YFLogxK
U2 - 10.11591/ijai.v10.i1.pp60-65
DO - 10.11591/ijai.v10.i1.pp60-65
M3 - Article
AN - SCOPUS:85103081197
VL - 10
SP - 60
EP - 65
JO - IAES International Journal of Artificial Intelligence
JF - IAES International Journal of Artificial Intelligence
SN - 2089-4872
IS - 1
ER -