Predicting Covid-19 using Random Forest Machine Learning Algorithm

ABSTRACT

COVID-19, also known as 2019-nCoV, is no longer a pandemic but an endemic disease that has killed many people worldwide. COVID-19 has no precise treatment or remedy at this time, but it is unavoidable to live with the disease and its implications. By quickly and efficiently screening for covid, one may determine whether or not one has COVID-19 and thus limit the financial and administrative burdens on healthcare systems. Research has shown that predictions which use many variables in order to predict the likelihood of infection have been established. Due to the world's inadequate healthcare systems, this fact places significant strain on these countries' healthcare systems, particularly in emerging nations. While there is no proven antiviral medication method or licensed vaccine that can eliminate the COVID-19 pandemic, there are other potential options that would alleviate both healthcare systems and the economy from the weight of the virus. Non-clinical approaches like machine learning, data mining, deep learning, and other artificial intelligence approaches are among the most promising approaches for use outside of a clinical setting. To make diagnosis and prognosis for patients with the 2019-NCoV pandemic easier, use these options. Additionally, artificial intelligence systems, such as decision trees, support vector machines, artificial neural networks, and naïve Bayesian models, are validated using a positive and negative COVID-19 case dataset. To establish the degree of connection between dependent characteristics, correlation coefficients between different dependent and independent variables were investigated. During preparation, the model was trained for 80% of the time, while at the same time, it was tested for 20% of the time. Based on the success evaluation, the Random Forest had the best precision of 94.99%. © 2021 IEEE.