Abstract : Shocking the world and still posing a threat to billions of lives is the viral sickness known as COVID-19. Coronavirus (COVID-19) detection has become increasingly important for doctors to do in recent years. COVID-19, unfortunately, spreads rapidly between humans, and within a few months might affect millions of individuals throughout the world. To stop the disease from spreading further, it is crucial to locate those who are afflicted as soon as possible. Despite the use of many medical tests for injury diagnosis, the hoped-for efficiency of detection has not yet been achieved. COVID-19 is a global epidemic that has already affected many countries. COVID-19 disease can cause symptoms ranging from quite minor to life-threatening. These symptoms are also intricate and difficult to predict. Using the severity score helps with suspect treatment that is heavily symptom-based. Fuzzy Expert System, one of the best approaches to modeling complex and uncertain systems, is used in this research to deal with this issue. Due to the recent nature of the pandemic and data around COVID-19. Most of the world's population was infected by COVID-19, and the virus had far-reaching consequences. It highlighted the insufficiency of available funds for medical care. diseases were rendered more nebulous by factors such as the wide variation in symptoms, preexisting diseases, age, diagnostic sophistication, and degree of uncertainty. Medical stakeholders, experts, hospitals, pharmaceutical companies, and others can all benefit from Fuzzy's ability to handle the fuzziness and ambiguity inherent in large amounts of patient data. When dealing with a situation involving ambiguity, incomplete information, or a lack of precision, many turn to Fuzzy Logic. In this research, we use the Fuzzy Inference System (FIS) in MATLAB to create a fuzzy expert system that can determine the severity of an infection from a patient's reported symptoms, which may include high body temperature, difficulty breathing, a dry cough, a diminished sense of smell or taste, and an inability to eat. Numerical and graphical representations of viral load as a function of symptom kind are presented. We have also covered the use of correlation and regression analysis to determine which symptoms are most strongly associated with a given viral load.
Cite : Kumar, N., & Goya, S. K. (2023). The Use of A Fuzzy Expert System for System-Based Covid-19 Viral Load Prediction (1st ed., pp. 206-221). Noble Science Press. https://doi.org/10.52458/9789388996570.2023.eb.ch042
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