Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study.

ABSTRACT: Cerebral-cardiac syndrome (CCS) is a severe cardiac complication following acute ischemic stroke, often associated with adverse outcomes. This study developed and validated a machine learning (ML) model to predict CCS using clinical, laboratory, and pre-extracted imaging features. A retrospective cohort of 511 post-stroke patients was analyzed. Data on demographics, laboratory results, imaging findings, and medications were collected. CCS diagnosis was based on cardiac dysfunction occurring after stroke, excluding pre-existing cardiac diseases. Five machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Deep Neural Network, were trained on 80% of the data and tested on the remaining 20%. Discrimination was assessed by AUC (95% CI),

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12368185 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature