Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models.

ABSTRACT:

Objectives

Stepwise linear regression (SLR) is the most common approach to predicting activities of daily living at discharge with the Functional Independence Measure (FIM) in stroke patients, but noisy nonlinear clinical data decrease the predictive accuracies of SLR. Machine learning is gaining attention in the medical field for such nonlinear data. Previous studies reported that machine learning models, regression tree (RT), ensemble learning (EL), artificial neural networks (ANNs), support vector regression (SVR), and Gaussian process regression (GPR), are robust to such data and increase predictive accuracies. This study aimed to compare the predictive accuracies of SLR and these machine learning models for FIM scores in stroke patients.

Methods

Subacute stroke patients

SUBMITTER: Miyazaki Y 

PROVIDER: S-EPMC10218720 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature