Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients
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Published:2021-08-31
Issue:6
Volume:13
Page:
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ISSN:2229-838X
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Container-title:The International Journal of Integrated Engineering
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language:
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Short-container-title:IJIE
Author:
Mazlan Sulaiman, ,Abdul Rahman Hisyam,Ksm Kader Ibrahim Babul Salam,Che Fai Yeong,Mohd Rostam Alhusni Nurul Aisyah, , , ,
Abstract
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.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,Materials Science (miscellaneous),Civil and Structural Engineering
Cited by
1 articles.
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1. Effect of Shoulder Movement on Assessing Upper Limb Performance of Stroke Patient;2021 IEEE 19th Student Conference on Research and Development (SCOReD);2021-11-23