Support Vector Machine Models for Cancer Detection and Related Clinical Applications Using Omics and Omics-Adjacent Data: A Systematic Review

Background

Support vector machines (SVMs) have been widely applied to high-dimensional molecular and related biomedical data for cancer detection and other clinical classification tasks. The evidence spans diverse cancer types, specimens, platforms, objectives, and validation designs, limiting direct comparison.