Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study

Abstract

Background: The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within medical data, enabling the identification of factors associated with etiological classification. However, there is currently a lack of research utilizing ML algorithms for predicting AIS etiology.

Objective: We aimed to use interpretable ML algorithms to develop AIS etiology prediction models, identify critical factors in etiology classification, and enhance existing clinical categorization.

Methods: This study involved patients with the Third China National Stroke Registry (CNSR-III). Nine models, which included Natural Gradient Boosting (NGBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and logistic regression (LR), were employed to predict large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE) using an 80:20 randomly split training and test set. We designed an SFS-XGB with 10-fold cross-validation for feature selection. The primary evaluation metrics for the models included the area under the receiver operating characteristic curve (AUC) for discrimination and the Brier score (or calibration plots) for calibration.

Results: A total of 5,213 patients were included, comprising 2,471 (47.4%) with LAA, 2,153 (41.3%) with SVO, and 589 (11.3%) with CE. In both LAA and SVO models, the AUC values of the ML models were significantly higher than that of the LR model (P < 0.001). The optimal model for predicting SVO (AUC [RF model] = 0.932) outperformed the optimal LAA model (AUC [NGB model] = 0.917) and the optimal CE model (AUC [LGBM model] = 0.846). Each model displayed relatively satisfactory calibration. Further analysis showed that the optimal CE model could identify potential CE patients in the undetermined etiology (SUE) group, accounting for 1,900 out of 4,156 (45.7%).

Conclusions: The ML algorithm effectively classified patients with LAA, SVO, and CE, demonstrating superior classification performance compared to the LR model. The optimal ML model can identify potential CE patients among SUE patients. These newly identified predictive factors may complement the existing etiological classification system, enabling clinicians to promptly categorize stroke patients' etiology and initiate optimal strategies for secondary prevention.

Keywords: Acute ischemic stroke; Clinical prediction; Etiological classification; Machine learning; Prospective cohort study.

Fig. 1

Study flowchart

Fig. 2

Feature selection plots of LAA, SVO and CE models (in the training set). A, the Feature selection diagram of the LAA etiology classification prediction models; B, the Feature selection diagram of the SVO etiology classification prediction models; C, the Feature selection diagram of the CE etiology classification prediction models

Fig. 3

ROC curves plots of LAA, SVO and CE models (in the test set). A, the ROC curves of the LAA etiology classification prediction models; B, the ROC curves of the SVO etiology classification prediction models; C, the ROC curves of the CE etiology classification prediction models

Fig. 4

Calibration curves for the top four models in each etiology classification model (in the test set). A, the calibration curves of the LAA etiology classification prediction models; B, the calibration curves of the SVO etiology classification prediction models

Fig. 5

SHAP summary plot of the LAA, SVO and CE models (in the test set). A, the SHAP summary plot of the LAA etiology classification prediction models; B, the SHAP summary plot of the CE etiology classification prediction models; C, the SHAP summary plot of the SVO etiology classification prediction models