Open AccessXGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage
Author(s) -
Ruoran Wang,
Jing Zhang,
Baoyin Shan,
Min He,
Jianguo Xu
Publication year - 2022
Publication title -
neuropsychiatric disease and treatment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.819
H-Index - 67
eISSN - 1178-2021
pISSN - 1176-6328
DOI - 10.2147/ndt.s349956
Subject(s) - medicine , logistic regression , glasgow coma scale , receiver operating characteristic , subarachnoid hemorrhage , glasgow outcome scale , algorithm , incidence (geometry) , pediatrics , surgery , physics , computer science , optics
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.