Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.

ABSTRACT:

Background

Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients.

Method

In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People's Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge.

SUBMITTER: Qu S 

PROVIDER: S-EPMC9094516 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature