Multi-angle Support Vector Survival Analysis with Neural Tangent Kernel Study

Traditional survival analysis methods have been widely used in medicine and various fields, but they must satisfy some statistical assumptions. Machine learning methods can overcome this weakness. Three support vector-based machine learning-survival analysis models are proposed with an actual cancer dataset. Experiments show that some machine learning models outperform the Cox statistical model. Accurate kernel complexity experiments are designed for different models, and the selected kernel functions are Poly, neural tangent kernel (NTK) and so on. The NTK kernel model with the highest accuracy is investigated in more depth, including computational complexity and robustness. Some interesting properties are found in these aspects that may provide new ideas for researchers.