An Ensemble Machine Learning Approach for Screening Covid-19 based on
  Urine Parameters
Behzad Moayedi Abdalsamad Keramatfar Mohammad Hadi Goldani Mohammad Javad Fallahi Alborz Jahangirisisakht Mohammad Saboori Leyla badiei
The rapid spread of COVID-19 and the emergence of new variants underscore the
importance of effective screening measures. Rapid diagnosis and subsequent
quarantine of infected individuals can prevent further spread of the virus in
society. While PCR tests are the gold standard for COVID-19 diagnosis, they are
costly and time-consuming. In contrast, urine test strips are an inexpensive,
non-invasive, and rapidly obtainable screening method that can provide
important information about a patient's health status. In this study, we
collected a new dataset and used the RGB (Red Green Blue) color space of urine
test strips parameters to detect the health status of individuals. To improve
the accuracy of our model, we converted the RGB space to 10 additional color
spaces. After evaluating four different machine learning models, we proposed a
new ensemble model based on a multi-layer perceptron neural network. Although
the initial results were not strong, we were able to improve the model's
screening performance for COVID-19 by removing uncertain regions of the model
space. Ultimately, our model achieved a screening accuracy of 80% based on
urine parameters. Our results suggest that urine test strips can be a useful
tool for COVID-19 screening, particularly in resource-constrained settings
where PCR testing may not be feasible. Further research is needed to validate
our findings and explore the potential role of urine test strips in COVID-19
diagnosis and management.