COVID-19 SYMPTOMS DATA ANALYSIS AND MODELING FROM HUMAN BIOMETRICS AND CURRENT HEALTH CONDITION

After the waves of several variants of COVID-19 that hit the world, it is necessary to explore ways to control the medical consequences of the virus and the factors that could affect or control those health issues. This could help in treating patients proactively and increasing the likelihood of controlling the infection. Thus, this paper aims to investigate the likelihood of relating biological characteristics, health condition and social status of COVID-19 infected people to the actual symptoms that resulted. The study collects data through an online questionnaire based on a cross-sectional form that gathered data from 1143 suspected COVID-19 infected participants from three countries, namely, Saudi Arabia, Egypt, and Pakistan. The collected data was preprocessed to exclude uninfected samples, resulting in 1088 confirmed infections. Then, a statistical analysis explores the relationships between the included factors and the possible appearance of symptoms. The results gave some insights into the relationship between biometrics, health records and social status of the patients. In addition, most symptoms appeared in patients who had not taken any vaccine, and the effects of the virus seem less severe with individuals who were vaccinated with one or two doses. This paper contributed to the field of knowledge by predicting the consequences of COVID-19 based on the physical characteristics of patients. The findings of this research would open a new research area of predicting symptoms based on biometrics.