Please use this identifier to cite or link to this item: https://doi.org/10.1109/access.2021.3067311
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
Authors: 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
Keywords: Coronavirus
COVID-19
Disease diagnosis
Feature selection
Slime mould algorithm
Support vector machine
Issue Date: 1-Jan-2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
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
Rights: Attribution 4.0 International
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.
Source Title: IEEE Access
URI: https://scholarbank.nus.edu.sg/handle/10635/232662
ISSN: 2169-3536
DOI: 10.1109/access.2021.3067311
Rights: Attribution 4.0 International
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