  The Reverse Transcription Polymerase Chain Reaction (RTPCR)} test is the
silver bullet diagnostic test to discern COVID infection. Rapid antigen
detection is a screening test to identify COVID positive patients in little as
15 minutes, but has a lower sensitivity than the PCR tests. Besides having
multiple standardized test kits, many people are getting infected and either
recovering or dying even before the test due to the shortage and cost of kits,
lack of indispensable specialists and labs, time-consuming result compared to
bulk population especially in developing and underdeveloped countries.
Intrigued by the parametric deviations in immunological and hematological
profile of a COVID patient, this research work leveraged the concept of
COVID-19 detection by proposing a risk-free and highly accurate Stacked
Ensemble Machine Learning model to identify a COVID patient from communally
available-widespread-cheap routine blood tests which gives a promising
accuracy, precision, recall and F1-score of 100%. Analysis from R-curve also
shows the preciseness of the risk-free model to be implemented. The proposed
method has the potential for large scale ubiquitous low-cost screening
application. This can add an extra layer of protection in keeping the number of
infected cases to a minimum and control the pandemic by identifying
asymptomatic or pre-symptomatic people early.
