Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke.

ABSTRACT:

Objective

To construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML).

Methods

Retrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The "glmnet", "rpart", "xgboost", "randomForest", "neuralnet" packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance.

Results

The post-clustering dataset co

SUBMITTER: Xing Y 

PROVIDER: S-EPMC10792580 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature