ANALYSIS ON CREATING MINIMUM CLASSIFICATION GUIDELINES FOR BREAST CANCER DIAGNOSIS THROUGH THE APPLICATION OF SUPPORT VECTOR MACHINE ALGORITHMS

Abstract

The Support Vector Machine, or SVM, is a linear model that can be used to solve classification and regression issues. It can solve both linear and nonlinear problems and is useful for a wide range of applications. SVM is a basic concept: The method divides the data into classes by drawing a line or hyperplane. The goal of this study is to see if putting past knowledge into an SVM classifier for Breast Cancer Diagnosis can increase its accuracy. In the diagnosis of breast cancer, a rule-based classifier is extensively utilized. In recent decades, a classifier with good illness classification performance has been developed, and it is in high demand. Because classification rules are derived from previous diagnostics with a large number of features, it is difficult to create a minimum number of high performance rules while retaining all diagnostic information. In this paper, we proposed a SVM model in order to provide the best performance classifier. Based on the experimental results of the SVM model, we can achieve an accuracy of 1.0.