An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study

• Published in: IEEE access Vol. 9; pp. 45486 - 45503
• Main 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
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blood; Computational and Artificial Intelligence; Computers and Information Processing; coronavirus; Coronaviruses; COVID-19; Decision making; Decision trees; Diagnosis; disease diagnosis; Diseases; feature selection; Lung; Machine learning; Medical diagnostic imaging; Performance indices; Prediction models; Slime; slime mould algorithm; support vector machine; Support vector machines; Viral diseases; COVID-19; coronavirus; disease diagnosis; slime mould algorithm; support vector machine; feature selection
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3067311

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.