Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

Abstract

We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems.