Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning
4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021
; : 277-281, 2021.
Article
in English
| Scopus | ID: covidwho-1722912
ABSTRACT
Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall 93.55%, specificity 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity. ©2021 IEEE
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021
Year:
2021
Document Type:
Article
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