CoySvM-(GeD): Coyote Optimization-Based Support Vector Machine Classifier for Cancer Classification Using Gene Expression Data

Abstract

Cancer, by any means, is a significant cause of death worldwide. In the analysis of cancer disease, the classification of different tumor types is very important. This test initiates an attitude to the classification of cancer through the data in gene expression by modeling the support vector machine. Genetic material expression data of individual tumor types is designed by the SVM classifier, which tends to increase the potential of genetic data. Feature selection has long been considered a practical standard since its introduction in the field, and numerous feature selection methods have been used in an effort to reduce the input dimension while enhancing the classification performance. The proposed optimization has pertained to the gene expression data that selects the fusion factors for the hybrid kernel function in the SVM classifier and the genes as informative for cancer classification. The analysis of cancer classification is performed using colon cancer and breast cancer, and the performance of CoySVM is tested by taking the measures as precision, recall, and F-measure, and it achieves 87.598%, 95.669%, and 98.088% for colon cancer in addition to 93.647%, 92.984%, and 95% for breast cancer. It shows the best performance due to its highest classification in selected measures than the conventional methods.