Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.
NeuroRehabilitation
; 2024 Jul 16.
Article
em En
| MEDLINE
| ID: mdl-39031394
ABSTRACT
BACKGROUND:
Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall.OBJECTIVE:
We aimed to determine the most accurate clinical machine learning (ML) algorithm for predicting fall-related balance factors and identifying RAGT attributes.METHODS:
We applied five ML algorithms- logistic regression, random forest, decision tree, support vector machine (SVM), and extreme gradient boosting (XGboost)- to a dataset of 105 post-stroke patients undergoing RAGT. The variables included the Berg Balance Scale score, walking speed, steps, hip and knee active torques, functional ambulation categories, Fugl- Meyer assessment (FMA), the Korean version of the Modified Barthel Index, and fall history.RESULTS:
The random forest algorithm excelled (receiver operating characteristic area under the curve; AUCâ=â0.91) in predicting balance improvement, outperforming the SVM (AUCâ=â0.76) and XGboost (AUCâ=â0.71). Key determinants identified were knee active torque, age, step count, number of RAGT sessions, FMA, and hip torque.CONCLUSION:
The random forest algorithm was the best prediction model for identifying fall-related balance and RAGT determinants, highlighting the importance of key factors for successful RAGT outcome performance in fall-related balance improvement.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article