International Journal of Medicine and Data

Objective This study, based on the MIMIC-IV database, aims to develop an interpretable machine learning model to predict the ICU mortality risk in stroke patients. Methods Acute ischemic stroke patients were extracted from the MIMIC-IV database based on ICD-9 and ICD-10 codes. Feature selection was performed using the LASSO regression algorithm. Seven machine learning algorithms were evaluated and compared using metrics such as AUC, accuracy, and F1 score to identify the optimal algorithm. The dataset was split into training and testing sets in a 7:3 ratio, and five-fold cross-validation was conducted on the training set. Hyperparameter optimization was performed using grid search to enhance algorithm performance. The predictive ability and generalization performance of the optimal algorithm were evaluated on the test set. SHAP analysis was used to interpret the impact of key features on ICU mortality risk. Results: A total of 1,998 acute ischemic stroke patients were extracted from the MIMIC-IV database, of which 436 (21.8%) died within 30 days of ICU admission. After evaluating and comparing the performance of multiple machine learning algorithms on the validation set, the XGBoost algorithm was selected as the optimal model. The data were divided into training and testing sets in a 7:3 ratio, and five-fold cross-validation with grid search was employed for hyperparameter optimization. The results showed that the XGBoost algorithm demonstrated excellent ICU mortality risk prediction performance and generalization ability on the test set (AUC = 0.821, 95% CI: 0.778–0.864; accuracy = 80.7%). SHAP analysis revealed that early invasive oxygen therapy and advanced age were the primary risk factors for increased ICU mortality in stroke patients. Conclusion The XGBoost algorithm shows strong potential for predicting ICU mortality risk in acute ischemic stroke patients. Moreover, SHAP analysis highlights the significant roles of early invasive oxygen therapy and advanced age in determining ICU mortality risk.

Acute ischemic stroke; Mortality risk prediction model; Machine learning; Intrinsic interpretability; MIMIC-IV database