Performance Comparison of the Kernels of Support Vector Machine Algorithm for Diabetes Mellitus Classification

Abstract

Diabetes Mellitus is a disease where the body cannot use insulin properly, so this disease is one of the health problems in various countries. Diabetes Mellitus can be fatal and can cause other diseases and even lead to death. Based on this, it is important to have prediction activities to find out a disease. The SVM algorithm is used in classifying Diabetes Mellitus diseases. The purpose of this study was to compare the accuracy, precision, recall, and F1-Score values of the SVM algorithm with various kernels and data preprocessing. The data preprocessing used included data splitting, data normalization, and data oversampling. This research has the benefit of solving health problems based on the percentage of Diabetes Mellitus and can be used as material for accurate information. The results of this study are that the highest accuracy was obtained by 80% obtained from the polynomial kernel, the highest precision was obtained by 65% which was also obtained from the polynomial kernel, and the highest recall was obtained by 79% obtained from the RBF kernel and the highest f1-score was obtained by 70% obtained from RBF kernel.

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