Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

The Multiple Linear Regression (MLR) is a predictive model that was commonly used to predict the clinical score of stroke patients. However, the performance of the predictive model slightly depends on the method of feature selection on the data as input predictor to the model. Therefore, appropriate feature selection method needs to be investigated in order to give an optimum performance of the prediction. This paper aims (i) to develop predictive model for Motor Assessment Scale (MAS) prediction of stroke patients, (ii) to establish relationship between kinematic variables and MAS score using a predictive model, (iii) to evaluate the prediction performance of a predictive model based on root mean squared error (RMSE) and coefficient of determination R2. Three types of feature selection methods involve in this study which are the combination of all kinematic variables, the combination of the best four or less kinematic variables, and the combination of kinematic variables based on p < 0.05. The prediction performance of MLR model between two assessment devices (iRest and ReHAD) has been compared. As the result, MLR model for ReHAD with the combination of kinematic variables that has p < 0.05 as input predictor has the best performance with Draw I (RMSEte = 1.9228, R2 = 0.8623), Draw Diamond (RMSEte = 2.6136, R2 = 0.7477), and Draw Circle (RMSEte = 2.1756, R2 = 0.8268). These finding suggest that the relationship between kinematic variables and MAS score of stoke patients is strong, and the MLR model with feature selection of kinematic variables that has p < 0.05 is able to predict the MAS score of stroke patients using the kinematic variables extracted from the assessment device

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores