A comparison of machine learning algorithms in predicting COVID-19 prognostics

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demogra...

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Bibliographic Details
Published in:Internal and emergency medicine 2023-01, Vol.18 (1), p.229-239
Main Authors: Ustebay, Serpil, Sarmis, Abdurrahman, Kaya, Gulsum Kubra, Sujan, Mark
Format: Article
Language:English
Subjects:
Algorithms
C-reactive protein
Calcium (blood)
Coronaviruses
COVID-19
COVID-19 - diagnosis
Decision making
Demography
EM - Original
Humans
Internal Medicine
Intubation
Lactic acid
Lymphocytes
Machine Learning
management and prognosis
Medicine
Medicine & Public Health
Patients
Prediction models
Prognosis
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Summary:ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data ( n  = 11,712), and (2) patient demographics, clinical data, and blood test results ( n  = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.
ISSN:1828-0447
1970-9366
DOI:10.1007/s11739-022-03101-x