Support Vector Machine Approaches for Tumor Classification and Survival Prediction in Cancer Patients: A Multi-Omics Data Analysis

Abstract

This study examines the viability of Support Vector Machine (SVM) calculations in tumour classification and survival forecast utilizing multi-omics information investigation in cancer patients. Leveraging a comprehensive dataset comprising genomic, transcriptomic, proteomic, and metabolomics profiles from assorted cancer sorts, we compared four SVM variations: Direct SVM, Polynomial SVM, Radial Basis Function (RBF) SVM, and Sigmoid SVM. Results illustrated that the RBF SVM calculation displayed predominant execution in tumour classification, accomplishing an exactness of 92%, with accuracy, review, and F1 score values of 91%, 94%, and 92% respectively. For survival forecast, the RBF SVM too beat other variations with a concordance file (C-Index) of 78%. These discoveries highlight the potential of SVM approaches in leveraging multi-omics information to move forward with cancer determination and forecast. Our consideration contributes to the developing body of research in machine learning-based cancer investigation and underscores the significance of coordination of different atomic datasets for personalized oncology.