Covid-19 (Coronavirus) Disease Diagnosis Using Fuzzy Inference System

Abstract

Intelligent Systems (IS) are called technologically advanced machines. IS includes a variety of techniques that can deal with uncertainty and complex problems. This research study aims to diagnose the coronavirus (COVID-19) diseases symptoms using an Intelligent Systems approach. The COVID-19 epidemic is currently one of the most deadly diseases in the world. The pandemic has affected human life and global economy adversely at a large scale. It is spreading rapidly through humans, causing severe health issues and death worldwide. This virus affects different people in different ways. A large number of people are not aware of being infected with COVID- 19, as some people are asymptomatic, or show initial symptoms that could be confused with a mild illness, such as having a cough or fever. There are over 3 million deaths currently caused by COVID-19, with the number increasing regularly. Advanced healthcare and error-free disease diagnosis are ubiquitous now-a-days as a result of technological advancement. Better future and healthy lifestyle are majorly depending on sensible and latest health care facilities. Large number of research and articles prove the efficiency and effectiveness of Intelligent Systems being used for diagnosing various symptoms of heart disease, cancer and diabetes. In this Thesis, an Intelligent System encompassing five Mamdani FISs is presented to help the COVID-19 disease diagnosis. This advanced Intelligent System will help to reduce the uncertainty as well as ease the diagnosing process. The proposed FISs can provide fuzzification results for the considered sub-systems with multiple inputs. In addition, a user-friendly interface is implemented for ease of interaction between a human and the proposed Intelligent System using the MATLAB software. The designed Intelligent System can decide the possible risk of getting COVID-19 disease. In each of the FISs, the fuzzification rules and database play a wide role. The 1st FIS considers four (Age, Medical Supplements, Immunity Strength, Previous Medical History) factors as input parameters to find out the “Disease Tendency” of COVID-19. Similarly, the input (Temperature, Tiredness, Dry Cough, Sore Throat) factors for the 2nd FIS, yield the “Most Common” symptoms. The 3rd FIS considers 4 input (Diarrhea, Headache, Conjunctivitis, Loss of Taste) factors yielding “Less Common” symptoms. The 4th FIS considers also 4 input (Breathing Difficulties, Chest Pain, Loss of Speech/Movement, Cholesterol Level) factors to yield “Serious Common” symptoms. Finally, the last FIS considers as inputs the: “Disease Tendency”, the “Most Common” symptoms, the “Less Common” symptoms, and the “Serious Common” symptoms; to yield as a result the disease likelihood (Consider changing serious common to something else. It doesn't really make sense. If you're just talking about more serious symptoms, maybe just have that category be called "Serious" instead of "Serious Common") Therefore, the overall proposed Intelligent System considers a total of 16 factors as input variables. It is important to notice the novel consideration of the Cholesterol Level as a factor in the “Serious Common” symptoms FIS module. It is mandatory to diagnose a disease in early stage to control and halt its spread. The proposed IS will help diagnose the COVID-19 symptoms and affection in early stage. Moreover, the advanced FIS is beneficial for an individual to diagnose disease by him/herself and extremely helpful in such places and societies where it is almost impossible to find the supply of physicians for the timely treatment of any medical disease.