A Small Scale COVID-19 Diagnosis Program: A Philippine Perspective

Abstract

The world is still currently facing a pandemic. In the Philippines, the number of cases is rapidly rising. Since there is yet a cure to be found, the best cure to such is prevention such as being aware of the adverse effects that it has on people along with the symptoms commonly felt by those who have the disease. Constant sanitation is also necessary to kill the bacteria causing the disease before it even has the chance to spread throughout the human body. In this research, a small scale AI program that could diagnose a person with the probability of having the disease was developed. Theprogram used patients' symptoms who have the disease, along with the corresponding severities of such, as input. Fuzzy logic was used in developing the program through the development and integration of a fuzzy inference system (FIS). Moreover, the testing accuracy of the proposed system was 70.83% which was based on the number of diagnoses that produced a medium or high verdict of a patient contracting the virus. The inputs for such diagnoses were the symptoms felt by confirmed COVID-19 patients along with their corresponding severities which were obtained from the data set acquired containing information regarding COVID-19 patients in the Philippines. Additionally, MATLAB was the software used to develop both the program and the FIS.