Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9022895/ |
| _version_ | 1818929495612063744 |
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| author | Guomin Liu Wenyuan Jia Mingjing Wang Ali Asghar Heidari Huiling Chen Yungang Luo Chengye Li |
| author_facet | Guomin Liu Wenyuan Jia Mingjing Wang Ali Asghar Heidari Huiling Chen Yungang Luo Chengye Li |
| author_sort | Guomin Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T03:45:43Z |
| format | Article |
| id | doaj.art-3f0670a46d0740a4bf8b2e4c9fd65fd6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:45:43Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3f0670a46d0740a4bf8b2e4c9fd65fd62022-12-21T19:54:38ZengIEEEIEEE Access2169-35362020-01-018468954690810.1109/ACCESS.2020.29781029022895Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector MachineGuomin Liu0Wenyuan Jia1Mingjing Wang2https://orcid.org/0000-0003-1985-4076Ali Asghar Heidari3https://orcid.org/0000-0001-6938-9948Huiling Chen4https://orcid.org/0000-0002-7714-9693Yungang Luo5Chengye Li6Department of Orthopedics, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Orthopedics, The Second Hospital of Jilin University, Changchun, ChinaInstitute of Research and Development, Duy Tan University, Da Nang, VietnamSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaJilin Provincial Changbai Mountain Anti-Tumor Medicine Engineering Center, Changchun, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaThis 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.https://ieeexplore.ieee.org/document/9022895/Support vector machinesine cosine algorithmcovariancecervical hyperextension injuryopposition-based learning |
| spellingShingle | Guomin Liu Wenyuan Jia Mingjing Wang Ali Asghar Heidari Huiling Chen Yungang Luo Chengye Li Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine IEEE Access Support vector machine sine cosine algorithm covariance cervical hyperextension injury opposition-based learning |
| title | Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine |
| title_full | Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine |
| title_fullStr | Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine |
| title_full_unstemmed | Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine |
| title_short | Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine |
| title_sort | predicting cervical hyperextension injury a covariance guided sine cosine support vector machine |
| topic | Support vector machine sine cosine algorithm covariance cervical hyperextension injury opposition-based learning |
| url | https://ieeexplore.ieee.org/document/9022895/ |
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