Predicting the mortality of patients with Covid-19: A machine learning approach.
Health Sci Rep
; 6(4): e1162, 2023 Apr.
Article
em En
| MEDLINE
| ID: mdl-37008820
ABSTRACT
Background and Aims:
Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease.Methods:
The data of this study were collected from hospitalized patients with COVID-19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling.Results:
GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places.Conclusion:
Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.
Texto completo:
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Bases de dados:
MEDLINE
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Health Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article