A prognostic model for classification of COVID-19 severity based on clinical and laboratory testing data
2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
; : 356-357, 2023.
Article
in English
| Scopus | ID: covidwho-2298570
ABSTRACT
This study aimed to build an machine learning based model to predict the COVID-19 severity and reveal risk factors related to COVID-19 severity based on laboratory testing and clinical data for 420 participants, using tree-based models such as XGBoost, LightGBM, random forest. We calculated the Odds Ratios (OR) to investigate whether the top-ranked features were statistically significant for severity classification, turning out that high sensitivity C-reactive protein (hs-CRP) was the most important feature for determining of COVID-19 severity and XGBoost model showed the highest performance in classifying COVID-19 severity and healthy controls with F1score (0.84) and AUC (0.87). We expect that our results are of considerable significance for early screening for diagnosing COVID-19 severity, which, in turn, assist in further retrospective research for uncommon infectious diseases. © 2023 IEEE.
clinical data; COVID-19; laboratory testing data; machine learning; prognostic model; Classification (of information); Diagnosis; Forestry; Random forests; Clinical testing; Laboratory testing; Learning Based Models; Machine-learning; Prognostic modeling; Risk factors; Testing data; Tree-based model
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Year:
2023
Document Type:
Article
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