Machine Learning Approaches to Predict Scoliosis

Ruixin Liang; Joanne Yip; Kai Tsun Michael To; Yunli Fan

doi:10.1007/978-3-030-80744-3_15

Machine Learning Approaches to Predict Scoliosis

Ruixin Liang
, Joanne Yip
, Kai Tsun Michael To
, Yunli Fan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

Scoliosis seriously affects the physical and mental health of patients. Therefore, machine learning approaches were used to predict whether the subject was scoliosis patient or not by physical characteristics and electromyography (EMG) ratios. One hundred and six subjects, including 33 healthy subjects and 73 subjects with scoliosis, have been involved in this study. However, only about half of the predictions were correct. This may because of the small dataset, and the relatively weak relationship between the features (age, height, weight, gender, and EMG ratios) and the occurrence of scoliosis. This present work served as an initial step for the application of artificial intelligence in scoliosis prediction. However, it is significant and necessary for a greater effort in this topic.

Original language	English
Title of host publication	Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021
Editors	Jay Kalra, Nancy J. Lightner, Redha Taiar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	116-121
Number of pages	6
ISBN (Print)	9783030807436
DOIs	https://doi.org/10.1007/978-3-030-80744-3_15
Publication status	Published - 25 Jul 2021
Event	AHFE Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021 - Virtual, Online Duration: 25 Jul 2021 → 29 Jul 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	263
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	AHFE Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021
City	Virtual, Online
Period	25/07/21 → 29/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Electromyography
Machine learning
Scoliosis

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

More information

10.1007/978-3-030-80744-3_15

Cite this

Liang, R., Yip, J., To, K. T. M., & Fan, Y. (2021). Machine Learning Approaches to Predict Scoliosis. In J. Kalra, N. J. Lightner, & R. Taiar (Eds.), Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021 (pp. 116-121). (Lecture Notes in Networks and Systems; Vol. 263). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80744-3_15

Liang, Ruixin ; Yip, Joanne ; To, Kai Tsun Michael et al. / Machine Learning Approaches to Predict Scoliosis. Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021. editor / Jay Kalra ; Nancy J. Lightner ; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2021. pp. 116-121 (Lecture Notes in Networks and Systems).

@inproceedings{d4a57d43f3c34b0a87ca3d461c91595f,

title = "Machine Learning Approaches to Predict Scoliosis",

abstract = "Scoliosis seriously affects the physical and mental health of patients. Therefore, machine learning approaches were used to predict whether the subject was scoliosis patient or not by physical characteristics and electromyography (EMG) ratios. One hundred and six subjects, including 33 healthy subjects and 73 subjects with scoliosis, have been involved in this study. However, only about half of the predictions were correct. This may because of the small dataset, and the relatively weak relationship between the features (age, height, weight, gender, and EMG ratios) and the occurrence of scoliosis. This present work served as an initial step for the application of artificial intelligence in scoliosis prediction. However, it is significant and necessary for a greater effort in this topic.",

keywords = "Electromyography, Machine learning, Scoliosis",

author = "Ruixin Liang and Joanne Yip and To, \{Kai Tsun Michael\} and Yunli Fan",

note = "Funding Information: Acknowledgments. This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP1-4), Hong Kong Special Administrative Region. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; AHFE Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021 ; Conference date: 25-07-2021 Through 29-07-2021",

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language = "English",

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editor = "Jay Kalra and Lightner, \{Nancy J.\} and Redha Taiar",

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Liang, R, Yip, J, To, KTM & Fan, Y 2021, Machine Learning Approaches to Predict Scoliosis. in J Kalra, NJ Lightner & R Taiar (eds), Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021. Lecture Notes in Networks and Systems, vol. 263, Springer Science and Business Media Deutschland GmbH, pp. 116-121, AHFE Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021, Virtual, Online, 25/07/21. https://doi.org/10.1007/978-3-030-80744-3_15

Machine Learning Approaches to Predict Scoliosis. / Liang, Ruixin; Yip, Joanne; To, Kai Tsun Michael et al.
Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021. ed. / Jay Kalra; Nancy J. Lightner; Redha Taiar. Springer Science and Business Media Deutschland GmbH, 2021. p. 116-121 (Lecture Notes in Networks and Systems; Vol. 263).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Machine Learning Approaches to Predict Scoliosis

AU - Liang, Ruixin

AU - Yip, Joanne

AU - To, Kai Tsun Michael

AU - Fan, Yunli

N1 - Funding Information: Acknowledgments. This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP1-4), Hong Kong Special Administrative Region. Publisher Copyright: © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2021/7/25

Y1 - 2021/7/25

N2 - Scoliosis seriously affects the physical and mental health of patients. Therefore, machine learning approaches were used to predict whether the subject was scoliosis patient or not by physical characteristics and electromyography (EMG) ratios. One hundred and six subjects, including 33 healthy subjects and 73 subjects with scoliosis, have been involved in this study. However, only about half of the predictions were correct. This may because of the small dataset, and the relatively weak relationship between the features (age, height, weight, gender, and EMG ratios) and the occurrence of scoliosis. This present work served as an initial step for the application of artificial intelligence in scoliosis prediction. However, it is significant and necessary for a greater effort in this topic.

AB - Scoliosis seriously affects the physical and mental health of patients. Therefore, machine learning approaches were used to predict whether the subject was scoliosis patient or not by physical characteristics and electromyography (EMG) ratios. One hundred and six subjects, including 33 healthy subjects and 73 subjects with scoliosis, have been involved in this study. However, only about half of the predictions were correct. This may because of the small dataset, and the relatively weak relationship between the features (age, height, weight, gender, and EMG ratios) and the occurrence of scoliosis. This present work served as an initial step for the application of artificial intelligence in scoliosis prediction. However, it is significant and necessary for a greater effort in this topic.

KW - Electromyography

KW - Machine learning

KW - Scoliosis

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DO - 10.1007/978-3-030-80744-3_15

M3 - Conference article published in proceeding or book

AN - SCOPUS:85112147109

SN - 9783030807436

T3 - Lecture Notes in Networks and Systems

SP - 116

EP - 121

BT - Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021

A2 - Kalra, Jay

A2 - Lightner, Nancy J.

A2 - Taiar, Redha

PB - Springer Science and Business Media Deutschland GmbH

T2 - AHFE Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021

Y2 - 25 July 2021 through 29 July 2021

ER -

Liang R, Yip J, To KTM, Fan Y. Machine Learning Approaches to Predict Scoliosis. In Kalra J, Lightner NJ, Taiar R, editors, Advances in Human Factors and Ergonomics in Healthcare and Medical Devices - Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, 2021. Springer Science and Business Media Deutschland GmbH. 2021. p. 116-121. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-80744-3_15

Machine Learning Approaches to Predict Scoliosis

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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