Journal
IEEE ACCESS
Volume 8, Issue -, Pages 46895-46908Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2978102
Keywords
Support vector machine; sine cosine algorithm; covariance; cervical hyperextension injury; opposition-based learning
Categories
Funding
- Science and Technology Plan Project of Wenzhou, China [ZG2017019]
- Medical and Health Technology Projects of Zhejiang Province, China [2019RC207]
- Special Health Project of Department of Finance of Jilin Province of China [3D518S533429, 3D5177703429]
- Jilin Provincial Economic Structure Strategic Adjustment and Guidance Special Fund Project [2014Y072]
- Provincial Special Health Project of Jilin Province of China [3D516D7034299]
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This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the COSCA method that combines the opposition-based learning mechanism and covariance mechanism to boost and recover the exploratory competence of SCA. The proposed COSCA approach is utilized to optimize the two critical parameters of the SVM, and it is also employed to catch the optimal feature subset. Based on the optimal parameter combination and feature subset, COSCA-SVM is able to make self-directed prediction of cervical hyperextension injury. The proposed COSCA was compared with other well-known and effective methods using 23 benchmark problems. Simulation results verify that the proposed COSCA is significantly superior to studied methods in dealing with majority of benchmark problems. Meanwhile, the proposed COSCA-SVM is compared with six other machine learning approaches considering a real-life dataset. Results have shown that the proposed COSCA-SVM can achieve better classification routine and higher stability on all four indicators. Therefore, we can expect that COSCA-SVM can be a promising building block for predicting cervical hyperextension injury.
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