tetano
Editor, Senior Moderator
J Med Virol
. 2021 Sep 20.
doi: 10.1002/jmv.27352. Online ahead of print.
Using Different Machine Learning Models to Classify Patients with Mild and Severe Cases of COVID-19 Based on Multivariate Blood Testing
Rui-Kun Zhang[SUP] 1 [/SUP], Qi Xiao[SUP] 1 [/SUP], Sheng-Lang Zhu[SUP] 2 [/SUP], Hai-Yan Lin[SUP] 2 [/SUP], Ming Tang[SUP] 3 [/SUP]
Affiliations
Abstract
Background: COVID19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system.
Method: 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 models to classify patients with either mild or severe cases of COVID-19.
Results: All models show good performance in the classification between COVID-19 patients with mild and severe disease. The 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.
Conclusion: Different machine learning models can classify patients with mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19. This article is protected by copyright. All rights reserved.
Keywords: Artificial intelligence < Biostatistics & Bioinformatics; Coronavirus < Virus classification; Infection.
. 2021 Sep 20.
doi: 10.1002/jmv.27352. Online ahead of print.
Using Different Machine Learning Models to Classify Patients with Mild and Severe Cases of COVID-19 Based on Multivariate Blood Testing
Rui-Kun Zhang[SUP] 1 [/SUP], Qi Xiao[SUP] 1 [/SUP], Sheng-Lang Zhu[SUP] 2 [/SUP], Hai-Yan Lin[SUP] 2 [/SUP], Ming Tang[SUP] 3 [/SUP]
Affiliations
- PMID: 34542195
- DOI: 10.1002/jmv.27352
Abstract
Background: COVID19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system.
Method: 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 models to classify patients with either mild or severe cases of COVID-19.
Results: All models show good performance in the classification between COVID-19 patients with mild and severe disease. The 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.
Conclusion: Different machine learning models can classify patients with mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19. This article is protected by copyright. All rights reserved.
Keywords: Artificial intelligence < Biostatistics & Bioinformatics; Coronavirus < Virus classification; Infection.