Staff View: Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation 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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Mazlan, Sulaiman, Abdul Rahman, Hisyam, Emhemed, Abdul Rahman A. A., Ahmmad, Siti Nor Zawani, Noordin, Muhammad Khair, Mohd. Rostam Alhusni, Nurul Aisyah, Abdullah, Muhammad Najib
Format:	Article
Language:	English
Published:	Penerbit UTHM 2023
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/105733/1/MuhammadKhairNoordin2023_LinearandNonLinearPredictiveModelsinPredictingMotor.pdf http://eprints.utm.my/105733/ http://dx.doi.org/10.30880/ijie.2023.15.04.020
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.105733
record_format	eprints
spelling	my.utm.1057332024-05-13T07:26:08Z http://eprints.utm.my/105733/ Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device. Mazlan, Sulaiman Abdul Rahman, Hisyam Emhemed, Abdul Rahman A. A. Ahmmad, Siti Nor Zawani Noordin, Muhammad Khair Mohd. Rostam Alhusni, Nurul Aisyah Abdullah, Muhammad Najib Q Science (General) 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. Penerbit UTHM 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/105733/1/MuhammadKhairNoordin2023_LinearandNonLinearPredictiveModelsinPredictingMotor.pdf Mazlan, Sulaiman and Abdul Rahman, Hisyam and Emhemed, Abdul Rahman A. A. and Ahmmad, Siti Nor Zawani and Noordin, Muhammad Khair and Mohd. Rostam Alhusni, Nurul Aisyah and Abdullah, Muhammad Najib (2023) Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device. International Journal of Integrated Engineering, 15 (4). pp. 237-247. ISSN 2229-838X http://dx.doi.org/10.30880/ijie.2023.15.04.020 DOI: 10.30880/ijie.2023.15.04.020
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Mazlan, Sulaiman Abdul Rahman, Hisyam Emhemed, Abdul Rahman A. A. Ahmmad, Siti Nor Zawani Noordin, Muhammad Khair Mohd. Rostam Alhusni, Nurul Aisyah Abdullah, Muhammad Najib Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
description	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.
format	Article
author	Mazlan, Sulaiman Abdul Rahman, Hisyam Emhemed, Abdul Rahman A. A. Ahmmad, Siti Nor Zawani Noordin, Muhammad Khair Mohd. Rostam Alhusni, Nurul Aisyah Abdullah, Muhammad Najib
author_facet	Mazlan, Sulaiman Abdul Rahman, Hisyam Emhemed, Abdul Rahman A. A. Ahmmad, Siti Nor Zawani Noordin, Muhammad Khair Mohd. Rostam Alhusni, Nurul Aisyah Abdullah, Muhammad Najib
author_sort	Mazlan, Sulaiman
title	Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
title_short	Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
title_full	Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
title_fullStr	Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
title_full_unstemmed	Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
title_sort	linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.
publisher	Penerbit UTHM
publishDate	2023
url	http://eprints.utm.my/105733/1/MuhammadKhairNoordin2023_LinearandNonLinearPredictiveModelsinPredictingMotor.pdf http://eprints.utm.my/105733/ http://dx.doi.org/10.30880/ijie.2023.15.04.020
_version_	1800082655519178752
score	13.211869

Linear and non-linear predictive models in predicting motor assessment scale of stroke patients using non-motorized rehabilitation device.

Similar Items