Research on Rehabilitation Effect Prediction for Patients with SCI Based on Machine Learning

Abstract

Because of the complex condition of patients with spinal cord injury (SCI), it is difficult to accurately calculate the activity of daily living (ADL) score of discharged patients. In view of the above problem, this research proposes a prediction model of discharged ADL score based on machine learning, in order to get the rehabilitation effect of patients after rehabilitation training. First, the medical records of 1231 patients with SCI were collected, and the corresponding data preprocessing was carried out. Secondly, the Pearson correlation coefficient method was combined with the feature selection method based on random forest (RF) to screen out 6 features closely related to the discharged ADL score. Then RF and RF optimized by Harris hawks optimizer (HHO-RF) were used to predict the discharged ADL score of patients with SCI. The mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) were used as evaluation indicators of the model. The prediction features selected by feature extraction were ADL score on admission, age, injury segment, injury reason, injury position, and injury degree. After 10-fold cross-validation, MAE, RMSE, and R2 of RF were 0.0875, 0.1346, and 0.7662, respectively. MAE, RMSE, and R2 of HHO-RF were 0.0821, 0.1089, and 0.8537, respectively. The prediction effect of HHO-RF has been greatly improved. In clinical treatment, HHO-RF can accurately predict the discharged ADL score and provide a reasonable direction for patients to choose rehabilitation programs.