ArticleIEEE access : practical innovations, open solutions2021
An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 25 citations in OpenAlex.
- Feature selection for effective prediction of SARS-COV-2 using machine learning.Genes & genomics · 2024Article
- Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction.Biomedical signal processing and control · 2023Article
- ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.Computers in biology and medicine · 2023Article
- Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.Computers in biology and medicine · 2022Article
- Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis.Computational and mathematical methods in medicine · 2022Article
- Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction.Journal of personalized medicine · 2021Article
- Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.Scientific reports · 2021Article
- Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation.Computers in biology and medicine · 2021Article
- Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?Computers in biology and medicine · 2021Article
- Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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