A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

Abstract

Breast cancer prediction is an important topic in the field of healthcare. Breast cancer is one of the most common cancers in women and early detection is critical for successful treatment. There are several methods for predicting breast cancer, including imaging studies, genetic testing, and risk assessment models. Early detection can greatly improve the chances of successful treatment and long-term survival. One approach to detecting breast cancer is to use machine learning algorithms such as support vector machine (SVM) classifiers. SVMs are a popular type of supervised learning algorithm that can be used for classification or regression analysis. To use SVMs for breast cancer classification, you need to first prepare the data by dividing it into training and testing sets. The training set is used to train the SVM model, and the testing set is used to evaluate the performance of the model. The SVM model learns to classify the data by adjusting the parameters of the kernel function. In this paper, the performance of Linear, Polynomial, Gaussian and Sigmoid machine-learning kernels in the Support Vector Machine method was investigated to determine which kernel classifier is better at diagnosing breast cancer. In addition, this study made usage of the Wisconsin Breast Cancer (Diagnostic) dataset that contains 569 occurrences and 32 features for analysis. The major objective of this study is to compare a variety of kernel classifiers to identify the one that provides the best accuracy. Linear kernel support vector machine was shown to have the highest accuracy (97.90%) and lowest false discovery rates in this investigation. In contrast, other kernels and classification algorithms show low performance, which may not be more accurate in breast cancer prediction.