Identification of Nonlinear Interaction Effects in Prostate Cancer Survival Using Machine Learning-Based Modeling

Shapley Additive Explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models (Lundberg et al. NIPS 2017). SHAP values have been applied in other fields, demonstrating superior consistency and concordance with human intuition compared to other interpretation approaches. We describe a novel application of SHAP values to the prediction of overall survival (OS) in prostate cancer...