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Stroke AI PLOS One by vinnywilson1549 is a document available to read on EtoBox.

This study developed machine learning-based models to predict the risk of stroke in coronary artery disease (CAD) patients undergoing coronary revascularization, using data from 5757 patients. The Catboost model demonstrated the highest predictive performance with an area under the curve (AUC) of 0.831 in the training set and 0.760 in the testing set, outperforming traditional logistic regression. The findings suggest that these models can effectively identify high-risk patients, potentially improving posto

Author

vinnywilson1549

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EN