Automatic Diagnosis of COVID-19 Based on Machine Learning
2nd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2023
; : 38-41, 2023.
Статья
в английский
| Scopus | ID: covidwho-2316571
ABSTRACT
The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.
COVID-19 diagnosis; machine learning; Random Forest; SVM; Diseases; Medical imaging; Nucleic acids; Pattern recognition; Support vector machines; Viruses; Automatic diagnosis; Corona virus disease 2019 diagnose; Detection and diagnosis; Diagnostic model; Machine-learning; Nucleic acid detection; On-machines; Random forests; Virus disease; Diagnosis
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Scopus
Язык:
английский
Журнал:
2nd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2023
Год:
2023
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Статья
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