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

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...

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Bibliographic Details
Published in:PloS one 2023-05, Vol.18 (5), p.e0286269-e0286269
Main Authors: Miyazaki, Yuta, Kawakami, Michiyuki, Kondo, Kunitsugu, Tsujikawa, Masahiro, Honaga, Kaoru, Suzuki, Kanjiro, Tsuji, Tetsuya
Format: Article
Language:English
Subjects:
Accuracy
Activities of Daily Living
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Biology and Life Sciences
Care and treatment
Classification
Comorbidity
Computer and Information Sciences
Datasets
Diagnosis
Gaussian process
Health aspects
Humans
Inpatients
Learning algorithms
Machine Learning
Medical prognosis
Medical research
Medicine and Health Sciences
Medicine, Experimental
Methods
Modelling
Neural networks
Patients
Physical Sciences
Prediction models
Prevention
Prognosis
Recovery of Function
Regression analysis
Rehabilitation
Research and Analysis Methods
Risk factors
Robustness (mathematics)
Root-mean-square errors
Statistical analysis
Stroke
Stroke (Disease)
Stroke - therapy
Stroke Rehabilitation
Support vector machines
Treatment Outcome
Variables
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Summary: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. Subacute stroke patients (N = 1,046) who underwent inpatient rehabilitation participated in this study. Only patients' background characteristics and FIM scores at admission were used to build each predictive model of SLR, RT, EL, ANN, SVR, and GPR with 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) values were compared between the actual and predicted discharge FIM scores and FIM gain. Machine learning models (R2 of RT = 0.75, EL = 0.78, ANN = 0.81, SVR = 0.80, GPR = 0.81) outperformed SLR (0.70) to predict discharge FIM motor scores. The predictive accuracies of machine learning methods for FIM total gain (R2 of RT = 0.48, EL = 0.51, ANN = 0.50, SVR = 0.51, GPR = 0.54) were also better than of SLR (0.22). This study suggested that the machine learning models outperformed SLR for predicting FIM prognosis. The machine learning models used only patients' background characteristics and FIM scores at admission and more accurately predicted FIM gain than previous studies. ANN, SVR, and GPR outperformed RT and EL. GPR could have the best predictive accuracy for FIM prognosis.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0286269