Journal Article

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

Kondo, Kunitsugu, Miyazaki, Yuta, Honaga, Kaoru, Kawakami, Michiyuki, Tsujikawa, Masahiro, Tsuji, Tetsuya, Suzuki, Kanjiro

2023

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: 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