Please use this identifier to cite or link to this item:
https://doi.org/10.1109/access.2021.3067311
DC Field | Value | |
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dc.title | An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study | |
dc.contributor.author | Wu, Peiliang | |
dc.contributor.author | Ye, Hua | |
dc.contributor.author | Cai, Xueding | |
dc.contributor.author | Li, Chengye | |
dc.contributor.author | Li, Shimin | |
dc.contributor.author | Chen, Mengxiang | |
dc.contributor.author | Wang, Mingjing | |
dc.contributor.author | Heidari, Ali Asghar | |
dc.contributor.author | Chen, Mayun | |
dc.contributor.author | Li, Jifa | |
dc.contributor.author | Chen, Huiling | |
dc.contributor.author | Huang, Xiaoying | |
dc.contributor.author | Wang, Liangxing | |
dc.date.accessioned | 2022-10-13T00:54:44Z | |
dc.date.available | 2022-10-13T00:54:44Z | |
dc.date.issued | 2021-01-01 | |
dc.identifier.citation | Wu, Peiliang, Ye, Hua, Cai, Xueding, Li, Chengye, Li, Shimin, Chen, Mengxiang, Wang, Mingjing, Heidari, Ali Asghar, Chen, Mayun, Li, Jifa, Chen, Huiling, Huang, Xiaoying, Wang, Liangxing (2021-01-01). An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study. IEEE Access 9 : 45486-45503. ScholarBank@NUS Repository. https://doi.org/10.1109/access.2021.3067311 | |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/232662 | |
dc.description.abstract | This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection. © 2021 American Institute of Physics Inc.. All rights reserved. | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scopus OA2021 | |
dc.subject | Coronavirus | |
dc.subject | COVID-19 | |
dc.subject | Disease diagnosis | |
dc.subject | Feature selection | |
dc.subject | Slime mould algorithm | |
dc.subject | Support vector machine | |
dc.type | Article | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1109/access.2021.3067311 | |
dc.description.sourcetitle | IEEE Access | |
dc.description.volume | 9 | |
dc.description.page | 45486-45503 | |
Appears in Collections: | Students Publications |
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