Liew, Bernard XW and Pfisterer, Florian and Rügamer, David and Zhai, Xiaojun (2024) Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics, 165. p. 111998. DOI https://doi.org/10.1016/j.jbiomech.2024.111998
Liew, Bernard XW and Pfisterer, Florian and Rügamer, David and Zhai, Xiaojun (2024) Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics, 165. p. 111998. DOI https://doi.org/10.1016/j.jbiomech.2024.111998
Liew, Bernard XW and Pfisterer, Florian and Rügamer, David and Zhai, Xiaojun (2024) Strategies to optimise machine learning classification performance when using biomechanical features. Journal of Biomechanics, 165. p. 111998. DOI https://doi.org/10.1016/j.jbiomech.2024.111998
Abstract
Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets – a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved.
Item Type: | Article |
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Uncontrolled Keywords: | Machine learning; Deep learning; Gait; Biomechanics; Orthopedic; Musculoskeletal pain |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 28 Feb 2024 12:33 |
Last Modified: | 30 Oct 2024 16:36 |
URI: | http://repository.essex.ac.uk/id/eprint/37889 |
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