Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.
J Med Virol
; 94(1): 357-365, 2022 01.
Article
in English
| MEDLINE | ID: covidwho-1544349
ABSTRACT
COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning (ML) models to classify patients into either mild or severe cases of COVID-19. All models show good performance in the classification between COVID-19 patients into mild and severe disease. The area under the curve (AUC) of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the naive Bayes model has the best performance. Different ML models can classify patients into mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Blood Chemical Analysis
/
Severity of Illness Index
/
Neural Networks, Computer
/
Support Vector Machine
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
J Med Virol
Year:
2022
Document Type:
Article
Affiliation country:
Jmv.27352
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