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Identification of risk factors for scoliosis in elementary school children using machine learning.

Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up...

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Main Authors: Che Rahmat, Ahmad Aizat, A. Jalil, Siti Zura, Syed Abd. Rahman, Sharifah Alwiah, Usman, Sahnius, Alam, Mohammad Shabbir
Format: Article
Language:English
Published: Penerbit UTHM 2023
Subjects:
L Education (General)
LC Special aspects of education
Online Access:http://eprints.utm.my/105722/1/SitiZuraAJalil2023_IdentificationofRiskFactorsforScoliosisinElementarySchool.pdf
http://eprints.utm.my/105722/
http://dx.doi.org/10.30880/ijie.2023.15.03.009
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spelling my.utm.1057222024-05-13T07:18:57Z http://eprints.utm.my/105722/ Identification of risk factors for scoliosis in elementary school children using machine learning. Che Rahmat, Ahmad Aizat A. Jalil, Siti Zura Syed Abd. Rahman, Sharifah Alwiah Usman, Sahnius Alam, Mohammad Shabbir L Education (General) LC Special aspects of education Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis. Penerbit UTHM 2023-07-31 Article PeerReviewed application/pdf en http://eprints.utm.my/105722/1/SitiZuraAJalil2023_IdentificationofRiskFactorsforScoliosisinElementarySchool.pdf Che Rahmat, Ahmad Aizat and A. Jalil, Siti Zura and Syed Abd. Rahman, Sharifah Alwiah and Usman, Sahnius and Alam, Mohammad Shabbir (2023) Identification of risk factors for scoliosis in elementary school children using machine learning. International Journal of Integrated Engineering, 15 (3). pp. 94-103. ISSN 2229-838X http://dx.doi.org/10.30880/ijie.2023.15.03.009 DOI: 10.30880/ijie.2023.15.03.009
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 L Education (General)
LC Special aspects of education
spellingShingle L Education (General)
LC Special aspects of education
Che Rahmat, Ahmad Aizat
A. Jalil, Siti Zura
Syed Abd. Rahman, Sharifah Alwiah
Usman, Sahnius
Alam, Mohammad Shabbir
Identification of risk factors for scoliosis in elementary school children using machine learning.
description Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis.
format Article
author Che Rahmat, Ahmad Aizat
A. Jalil, Siti Zura
Syed Abd. Rahman, Sharifah Alwiah
Usman, Sahnius
Alam, Mohammad Shabbir
author_facet Che Rahmat, Ahmad Aizat
A. Jalil, Siti Zura
Syed Abd. Rahman, Sharifah Alwiah
Usman, Sahnius
Alam, Mohammad Shabbir
author_sort Che Rahmat, Ahmad Aizat
title Identification of risk factors for scoliosis in elementary school children using machine learning.
title_short Identification of risk factors for scoliosis in elementary school children using machine learning.
title_full Identification of risk factors for scoliosis in elementary school children using machine learning.
title_fullStr Identification of risk factors for scoliosis in elementary school children using machine learning.
title_full_unstemmed Identification of risk factors for scoliosis in elementary school children using machine learning.
title_sort identification of risk factors for scoliosis in elementary school children using machine learning.
publisher Penerbit UTHM
publishDate 2023
url http://eprints.utm.my/105722/1/SitiZuraAJalil2023_IdentificationofRiskFactorsforScoliosisinElementarySchool.pdf
http://eprints.utm.my/105722/
http://dx.doi.org/10.30880/ijie.2023.15.03.009
_version_ 1800082653899128832
score 13.211869

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