Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests.

ABSTRACT:

Background

Prognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging.

Objective

This study aimed to develop a model to identify the cause of stroke using machine learning (ML) methods and test its accuracy.

Methods

We retrospectively reviewed the data of patients who had determined etiology defined by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) from CASE-II (NCT04487340) to train and evaluate six ML models, namely, Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Ada Boosting, Gradient Boosting Machine (GBM), for the detection of cardioembolism (CE), large-artery atherosclerosis (LAA), an

SUBMITTER: Wang J 

PROVIDER: S-EPMC9051333 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature