Prediction System for Covid-19 Upcoming Cases Using Ensemble Classification

Abstract

An epidemic of the novel destructive Coronavirus has been spreading rapidly around the world since 2019 and has caused a great number of deaths. Providing patients with appropriate and most timely care is crucial to combating COVID-19 spread. Testing for the disease must be done quickly and accurately. Therefore, this paper developed an ensemble classification-based country-wise COVID-19 upcoming cases prediction model. This ensemble classification and prediction model shows the upcoming month's Corona virus cases, including newly confirmed cases, recovered cases, and deaths. This analysis is carried out based on these three cases occurring in different countries on sequential dates. The proposed model uses three famous classifiers, namely ANN, Gaussian Process and SVM which have different learning characteristics and architectures at the first stage. In the second stage, they combine their predictions with average calculations. Training and assessment of the proposed model were conducted using 75065 observations comprised of 61 features from John Hopkins University in Maryland. For data preparation, the envisioned work clusters the dataset based on world countries affected by COVID-19 separately. As a result, this set of clusters fetched data once again based on death, newly confirmed, and recovered cases. The experimental result shows the proposed ensemble model provides better performance when compared with previous classification algorithms.