Please use this identifier to cite or link to this item:
Title: Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression
Authors: Blagojevic, Andjela
Sustersic, Tijana
Lorencin, Ivan
Baressi Šegota, Sandi
Anđelić, Nikola
Milovanovic, Dragan
Baskić Danijela
Baskic, Dejan
Zdravković Petrović Nataša
Sazdanovic, Predrag
Car, Zlatan
Filipovic, Nenad
Issue Date: 2021
Abstract: Background and objectives: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary. Methods: We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19. Results: The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition. Conclusions: The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.
Type: article
DOI: 10.1016/j.compbiomed.2021.104869
ISSN: 0010-4825
SCOPUS: 2-s2.0-85115011860
Appears in Collections:Faculty of Engineering, Kragujevac
Faculty of Medical Sciences, Kragujevac

Page views(s)




Files in This Item:
File Description SizeFormat 
  Restricted Access
29.86 kBAdobe PDFThumbnail

Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.