Mathematical realization of diagnosing COVID-19 using Boolean Algebra

Daeuk Kim, Maria Nessie Sobina Chiang Yu, Ryan Rhay Vicerra, Raouf N.G. Naguib, Ronnie Concepcion II
De La Salle University, Manila, Philippines/South Korea
De La Salle University, Manila, Philippines
Liverpool Hope University, Liverpool, United Kingdom
Corresponding Email: [email protected]

COVID-19 has caused countless deaths across the globe. In developing countries like the Philippines, limited access to health services like ICU beds and PCR tests contributed more to COVID-19 related deaths. It is for this reason that the researchers developed a simple COVID-19 diagnostic tool using basic logic gates to determine whether one has COVID-19 or other related illnesses like flu, colds, and allergy. The researchers first collected information regarding the common symptoms of COVID-19 and similar diseases. The identified symptoms cough, fever, fatigue, loss of taste, and smell were used as the inputs for the circuit, while CODI-19 and other related diseases served as the output. The classification of symptoms was divided into often, sometimes, rarely, and never. In order to generate binary digits, often and sometimes were considered positive symptoms (1) while rarely and never were considered negative (0). Minterms were determined through the truth table of the conceptualized circuit. Furthermore, these are used to generate the Karnaugh map. Consequently, simplifying the Boolean expression for each output variable. This is a mathematical realization through Boolean algebra. Through the logic circuit created from Boolean expression, the researchers were able to successfully predict the expected disease based on the existence of symptoms. Furthermore, the researchers were able to translate the circuit into its complementary metal-oxide-semiconductor (CMOS) counterpart. While the designed tool is affordable and can be easily implemented, however, it still possesses a limitation as other COVID-19 positive patients are asymptomatic. Furthermore, the diagnostic tool was not tested on real-world data. Hence, the accuracy of the tool is based on theoretical experiments only.

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