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
UR - http://www.scopus.com/inward/record.url?scp=85112147109&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80744-3_15
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 -