XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

Abstract

Background Patients suffered aneurysmal subarachnoid hemorrhage (aSAH) usually develop poor survival and functional outcome. Evaluating aSAH patients at high risk of poor outcome is necessary for clinicians to make suitable therapeutical strategy. This study is conducted to develop prognostic model using XGBoost (extreme gradient boosting) algorithm in aSAH. Methods A total of 351 aSAH patients admitted to West China hospital were identified. Patients were divided into training set and test set with ratio of 7:3 to testify the predictive value of XGBoost based prognostic model. Additionally, logistic regression model was also constructed and compared with XGBoost based model. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity were calculated to evaluate the value of XGBoost and logistic regression. Results There were 74 (21.1%) non-survivors and 148 (42.1%) patients with unfavorable functional outcome. Non-survivors had older age (p=0.025), lower Glasgow coma scale (GCS) (p<0.001), higher World Federation of Neurosurgical Societies WFNS score (p<0.001), mFisher score (p<0.001). The incidence of intraventricular hemorrhage (IVH) (p=0.025) and delayed cerebral ischemia (DCI) (p<0.001) was higher in non-survivors than survivors. The AUC of XGBoost model for predicting mortality and unfavorable functional outcome were 0.950 and 0.958, which were higher than 0.767 and 0.829 of logistic regression model. Conclusion XGBoost based model is more precise than logistic regression model in predicting outcome of aSAH patients. Using XGBoost prognostic model is helpful for clinicians to identify high-risk aSAH patients and therefore strengthen medical care.

Field Measures Are All You Need: Predicting Need for Surgery in Elderly Ground-Level Fall Patients via Machine Learning

“…8 In recent literature, Wang et al use XGBoost as an interpretable classification model for the prediction of patient outcomes in subarachnoid hemorrhage. 9 This confirms other research demonstrating that XGBoost significantly outperforms logistic regression and t-tests in predicting outcomes. 8-11…”

“…8 In recent literature, Wang et al use XGBoost as an interpretable classification model for the prediction of patient outcomes in subarachnoid hemorrhage. 9 This confirms other research demonstrating that XGBoost significantly outperforms logistic regression and t-tests in predicting outcomes. [8][9][10][11] This study explores the utility of XGBoost in classifying surgery outcomes of GLFs to aid in the field triage of patients over 75 years old.…”

“…9 This confirms other research demonstrating that XGBoost significantly outperforms logistic regression and t-tests in predicting outcomes. [8][9][10][11] This study explores the utility of XGBoost in classifying surgery outcomes of GLFs to aid in the field triage of patients over 75 years old. We find that the factors the XGBoost model deems most important to classifying outcomes are >90% accurate and differ from the factors identified using T-tests.…”

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning

“…38 Our best performing algorithm was XGBoost, which has unique advantages including the avoidance of overfitting and faster computing while maintaining precision. 39,40 Furthermore, XGBoost works well with structured data, which may explain why it outperformed more complex algorithms such as MLP ANN on our dataset. 41 Third, our model performance remained robust on subgroup analysis of specific demographic and clinical populations.…”

“…This is particularly important in health care data, as patient outcomes can be influenced by many factors 38 . Our best performing algorithm was XGBoost, which has unique advantages including the avoidance of overfitting and faster computing while maintaining precision 39,40 . Furthermore, XGBoost works well with structured data, which may explain why it outperformed more complex algorithms such as MLP ANN on our dataset 41 .…”