The XGBoost Model Versus the Logistic Regression Model Created Based on Serum Markers in Predicting the Risk of Post‐Stroke Cognitive Impairment Following Acute Ischemic Stroke

Based on serum biomarkers, the XGBoost model can accurately predict the risk of PSCI in patients with acute ischemic stroke, with superior performance than the LR model, and may serve as a reliable tool for early identification to improve the diagnosis.From 261 acute ischemic stroke patients (training n = 183, testing n = 78), we collected demographic data, cognitive assessments, and serum indicators. LassoCV identified sensitive predictors including VE-Cad, NIHSS score, CRP, age, drinking history, and education years. The XGBoost model demonstrated superior performance over LR in predicting PSCI risk. SHAP analysis revealed how these variables influenced model predictions. Based on serum biomarkers, the XGBoost model accurately predicts PSCI risk and may serve as a reliable tool for early identification to improve diagnosis.