Table 2_Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke.xlsx

Hyperparameters were optimized via 10-fold cross-validation, and model performance was evaluated in an internal validation cohort (30% of the sample). Primary evaluation metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F1 score, and net benefit from decision curve analysis (DCA). The SHAP method was used to interpret the optimal model.</p>Results<p>A total of 161 patients (14.0%) developed END.

Feature selection ultimately identified five key predictors: ischemic stroke etiological subtype, Oxford Community Stroke Project (OCSP) classification, age, atrial fibrillation history, and prior stroke history. In both the development and internal validation cohorts, the logistic regression model demonstrated favorable and stable performance (development cohort AUC: 0.787, 95% CI: 0.735–0.839; internal validation cohort AUC: 0.751, 95% CI: 0.668–0.834), with a low log-loss value.

DCA suggested potential clinical utility of the logistic regression model. SHAP analysis revealed that the etiological subtype of ischemic stroke and age were the features contributing most to the model’s predictions.</p>Conclusion<p>This study successfully developed and validated a logistic regression model for predicting END risk. The model incorporates five routinely available clinical variables and demonstrates satisfactory predictive performance and interpretability.

The developed online tool may assist clinicians in early risk stratification, providing a reference for personalized intervention.</p>