ArticleJournal of neuroengineering and rehabilitation2025
Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.Brain sciences · 2026Review
- Article
- Development and validation of multiple machine learning models for identifying factors associated with walking ability in ischemic stroke patients: a single-center retrospective study with SHAP approach.Frontiers in neurology · 2026Article
- Income, psychological security, and subjective well-being in urban China: a machine learning analysis with SHAP interpretation.BMC psychology · 2025Article
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke.
methodsBetween May and November 2023, 237 patients with acute anterior circulation ischemic stroke were enrolled. Patients were randomly divided into training and validation sets at a 7:3 ratio. Thirty-one medical characteristics were collected, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to screen predictor variables. Predictive models were developed using the Random Survival Forest (RSF) and COX regression methods. The optimal model was identified based on C-index values. The SHapley Additive exPlanations (SHAP) method was employed to interpret the RSF model globally and locally.
resultsTen predictors were identified through LASSO regression, including age, gender, periventricular white matter hyperintensities (PVWMH), Montreal Cognitive Assessment (MoCA), National Institutes of Health Stroke Scale (NIHSS), enlarged perivascular spaces in basal ganglia (BG-EPVS), lacunes, parietal infarction, basal ganglia infarction, and Timed Up & Go (TUG) test score. The C-index values of the COX regression and RSF models were 0.741 and 0.761 in the training set and 0.705 and 0.725 in the validation set, respectively. SHAP analysis of the RSF model identified BG-EPVS, TUG, MoCA, age, and PVWMH as the top five most influential predictors of gait recovery.
conclusionThe RSF model demonstrated superior performance to the COX regression model in predicting gait recovery, offering a reliable tool for clinical decision-making regarding stroke patients' prognoses.
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Registered trials
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