Cancer datasets are often high-dimensional, comprising numerous features such as gene expression levels, imaging data, and various clinical parameters. SVMs are well-suited for high-dimensional spaces and can effectively handle datasets with many features. Additionally, SVMs are robust to overfitting, especially in cases where the number of dimensions exceeds the number of samples.
