Abstract
Objective : While the coronavirus persists marginally for
ninety-five percent of the infected case count, the remaining five
percent have been placed in a critical or vital condition. 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.
Material and Methods: A public dataset of a cross-sectional
study included the demographic and symptomatological characteristics of
223 COVID-19 patients. The dataset was randomly divided into training
(75%) and testing (25%) datasets. During training, the class imbalance
problem was solved, and the related factors with the COVID-19 severity
were selected using the evolutionary method supported by a genetic
algorithm. Neural Network (NN), Support Vector Machine (SVM), QUEST
algorithms together with confidence weighted voting, voting, and highest
confidence wins strategies (HCWS) were constructed, and the predictive
power of models was evaluated by performance metrics.
Results : Of the individual models, the NN model outperformed
SVM and QUEST algorithms based on the performance metrics in the
training and testing datasets. However, ensemble approaches gave better
predictions as compared to the individual models regarding all the
evaluation metrics.
Conclusions: 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.
Keywords: Classification, COVID-19 severity, ensemble learning,
machine learning, prediction.