Predicting internal carotid artery system risk based on common carotid artery by machine learning.

Early identification of internal carotid artery (ICA) system diseases is critical for preventing stroke and other cerebrovascular events. Traditional diagnostic methods rely heavily on clinician expertise and costly imaging, limiting accessibility. This study aims to develop an interpretable machine learning (ML) model using common carotid artery (CCA) features to predict ICA disease risk, enabling efficient screening. Clinical data from 1612 patients (806 high-risk vs. 806 low-risk ICA disease) were analyzed. CCA features-blood flow, intima-media thickness, internal diameter, age, and gender-were used to train five ML models. Model performance was evaluated via accuracy, sensitivity, specificity, AUC-ROC, and F1 score. SHAP analysis identified key predictors. The support vector machine (SVM) achieved optimal performance (accuracy, 84.9%; AUC, 92.6%), outperforming neural networks (accuracy, 81.4%; AUC, 89.8%). SHAP analysis revealed CCA blood flow (negative correlation) and intima-media thickness (positive correlation) as dominant predictors. This study demonstrates that CCA hemodynamic and structural features, combined with interpretable ML models, can effectively predict ICA disease risk. The SVM-based framework offers a cost-effective screening tool for early intervention, particularly in resource-limited settings. Future work will validate these findings in multi-center cohorts.