Interpretable Machine Learning for Predicting Early Neurological Deterioration in Symptomatic Intracranial Atherosclerotic Stenosis

Yang Yang¹Chunhao Mei¹Xiaoning Guo¹Jiajia Chen²Tingting Tao^1*Qingguang Wang^1*

• ¹Jiangyin Clinical College of Xuzhou Medical University, Jiangyin, China
• ²Jiangyin Fifth People's Hospital, jiangyin, China

Background: To develop and validate a machine learning (ML) model for early neurological deterioration (END) risk prediction in patients with symptomatic intracranial atherosclerotic stenosis (SICAS). Methods: This retrospective cohort study enrolled 557 patients with SICAS between January 2022 and December 2024. Relevant clinical data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression selected predictive features from clinical/imaging variables. Five ML algorithms, including Gaussian Naive Bayes (GNB), Gradient Boosting Decision Trees (GBDT), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Logistic Regression (LR), were trained (70% of the data) and validated (30% of the data) using 10-fold cross-validation. Model performance was assessed using the area under the curve (AUC), calibration, and decision curve analysis (DCA). Shapley additive explanations (SHAP) interpreted the feature contributions. Results: The overall incidence rate of END was 18.13%. The XGBoost model outperformed the other models, achieving a validation AUC of 0.874 (95% CI, 0.809–0.939), a sensitivity of 0.749, a specificity of 0.859, and excellent calibration (deviation: 0.116). DCA indicates the clinical utility of the XGBoost model. Key predictors included the NIHSS score (strongest driver), vascular stenosis severity, Triglyceride Glucose (TyG) index, age, initial systolic blood pressure (SBP), and diabetes. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of END in SICAS. Conclusion: This study demonstrates the potential of ML in predicting END in SICAS patients. The SHAP method enhances the interpretability of the prediction model, providing a practical and implementable solution for the early identification of high-risk patients.