Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI
Journal:
arXiv
Published Date:
Apr 10, 2025
Abstract
This study addresses the challenge of predicting post-stroke rigidity by
emphasizing feature interactions through graph-based explainable AI.
Post-stroke rigidity, characterized by increased muscle tone and stiffness,
significantly affects survivors' mobility and quality of life. Despite its
prevalence, early prediction remains limited, delaying intervention. We analyze
519K stroke hospitalization records from the Healthcare Cost and Utilization
Project dataset, where 43% of patients exhibited rigidity. We compare
traditional approaches such as Logistic Regression, XGBoost, and Transformer
with graph-based models like Graphormer and Graph Attention Network. These
graph models inherently capture feature interactions and incorporate intrinsic
or post-hoc explainability. Our results show that graph-based methods
outperform others (AUROC 0.75), identifying key predictors such as NIH Stroke
Scale and APR-DRG mortality risk scores. They also uncover interactions missed
by conventional models. This research provides a novel application of
graph-based XAI in stroke prognosis, with potential to guide early
identification and personalized rehabilitation strategies.
