Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
This addresses the problem of limited practical decision-making and risk analysis in survival modeling for researchers and practitioners, though it appears incremental as it builds on existing models.
The study tackled the lack of interpretability in functional survival models like Functional Survival Trees and Forests, introducing methods that produced efficient and easy-to-understand decision trees that accurately captured model decision-making processes.
Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the Functional Survival Tree (FST) and the Functional Random Survival Forest (FRSF). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Using both real and simulated datasets, the results demonstrate that the proposed approaches yield efficient, easy-to-understand decision trees that accurately capture the underlying decision-making processes of the model ensemble.