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Machine Learning Approaches to Predict Scoliosis

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Advances in Human Factors and Ergonomics in Healthcare and Medical Devices (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 263))

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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.

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Acknowledgments

This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP1-4), Hong Kong Special Administrative Region.

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Authors and Affiliations

  1. Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong, Special Administrative Region of China

    Ruixin Liang & Joanne Yip

  2. Insititue of Textile and Clothing, Faculty of Applied Science and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, Special Administrative Region of China

    Joanne Yip

  3. Department of Orthopaedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, Special Administrative Region of China

    Kai-Tsun Michael To & Yunli Fan

Authors
  1. Ruixin Liang
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  2. Joanne Yip
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  3. Kai-Tsun Michael To
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  4. Yunli Fan
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Editor information

Editors and Affiliations

  1. Dept.of Pathology, College of Medicine, Univ.of Saskatchewan Royal Univ.Hospital, Saskatoon, SK, Canada

    Jay Kalra

  2. Center for Applied Sys. Engineering, Veterans Affairs, Indianapolis, IN, USA

    Nancy J. Lightner

  3. Campus du Moulin de la Housse, Université de Reims Champagne-Ardenne, Reims Cedex 2, France

    Redha Taiar

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Liang, R., Yip, J., To, KT.M., Fan, Y. (2021). Machine Learning Approaches to Predict Scoliosis. In: Kalra, J., Lightner, N.J., Taiar, R. (eds) Advances in Human Factors and Ergonomics in Healthcare and Medical Devices. AHFE 2021. Lecture Notes in Networks and Systems, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-030-80744-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-80744-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80743-6

  • Online ISBN: 978-3-030-80744-3

  • eBook Packages: EngineeringEngineering (R0)

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