WHAT’S NEW? (what does this study contribute to the literature?)
Understanding clinical/demographic features, progression, and prognosis
of the COVID-19 disease may help recognize critically ill patients,
provide appropriate care, and avoid mortality. In light of this
important data, this research aims to design an intelligent model that
predicts the disease severity level by modeling the relationships
between the severity of COVID-19 infection and the various
demographic/clinical characteristics of individuals. The possible
contributions of the current study are given below:
- This study investigates to design an intelligent model that predicts
the disease severity level by modeling the relationships between the
severity of COVID-19 infection and the various demographic/clinical
characteristics of individuals.
- Neural Network (NN), Support Vector Machine (SVM), QUEST algorithms
together with confidence weighted voting, voting, and highest
confidence wins strategies (HCWS) were constructed.
- The proposed voting ensemble model outperforms other ensemble and
individual machine learning approaches for the severity prediction of
COVID-19 disease.
- The proposed ensemble learning model can be integrated into web or
mobile applications in classifying the severity of COVID-19 for
clinical decision support.