Prediction of 180-day post-admission's GOS in patients with spontaneous intracerebral hemorrhage based on explainable machine learning technology.

ABSTRACT: Effective outcome prediction is crucial in the management of spontaneous intracerebral hemorrhage (SICH). This study developed machine learning models to predict the 180-day Glasgow Outcome Scale (GOS) score using clinical parameters and identify key prognostic factors. All patients with SICH were randomized into training and internal validation cohortsin a 7:3 ratio. After the most relevant variables were selected by the SVM-RFE and LASSO algorithms, the predictive efficiency of the gradient boosting decision tree (GBDT), XGBoost, random forest (RF), and lightgbm models was evaluated through an exhaustive suite of performance indicators. The Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) methods were engaged to explain the top-performingmod

SUBMITTER: Yuan C 

PROVIDER: S-EPMC12643675 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature