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What is XGBoost for Aneurysmal Hemorrhage Prognosis about?

This study developed a prognostic model using the XGBoost machine learning algorithm to predict outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH). The model was tested on 351 patients and demonstrated superior predictive accuracy compared to traditional logistic regression, with area under the curve (AUC) values of 0.950 for mortality and 0.958 for unfavorable functional outcomes. The findings suggest that XGBoost can effectively identify high-risk aSAH patients, aiding clinicians in treat

Author

bh

Language

EN