A predictive model based on random forest for shoulder-hand syndrome.

ABSTRACT:

Objectives

The shoulder-hand syndrome (SHS) severely impedes the function recovery process of patients after stroke. It is incapable to identify the factors at high risk for its occurrence, and there is no effective treatment. This study intends to apply the random forest (RF) algorithm in ensemble learning to establish a predictive model for the occurrence of SHS after stroke, aiming to identify high-risk SHS in the first-stroke onset population and discuss possible therapeutic methods.

Methods

We retrospectively studied all the first-onset stroke patients with one-side hemiplegia, then 36 patients that met the criteria were included. The patients' data concerning a wide spectrum of demographic, clinical, and laboratory data were analyzed. RF algorithms were built to predic

SUBMITTER: Yu S 

PROVIDER: S-EPMC10102379 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature