Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

ABSTRACT:

Objective

Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP).

Methods

A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH.

SUBMITTER: Shu L 

PROVIDER: S-EPMC10968679 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature