Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial in hepatitis classification. These two functions were chosen due to their popularity any kernel-based machine learning method for solving classification task. The dataset then used evaluate performance of both methods that expected provide an accurate diagnosis patients obtain treatment at early phase. data obtained from hospitals Indonesia, consisting 89 hepatitis-B 31 hepatitis-C samples. analyzed several cases k-fold cross-validation, performances compared according accuracy, sensitivity, precision, F1-Score, running time. From experiments, it was concluded RBF is better with 6% increment 13% enhancement 5% improvement F1-Score. On other side, precision 2% higher than function. According results, use or medoids can be considered primary goal classification.</span></span>