A Supervised Multi-tree XGBoost Model for an Earlier COVID-19 Diagnosis Based on Clinical Symptoms

Abstract

Efficient screening of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) enables quick and efficient diagnosis of SARS-CoV-2 and can mitigate the burden on healthcare systems. The aim was to assist the medical team globally in triaging incoming patients, especially in countries with limited healthcare infrastructure. In this context, the features with imminent infection risk (Test Indication, Fever, and Headache) were obtained using a multi-tree XGBoost algorithm. Based on their feature importance, the top three clinically relevant earlier clinical symptoms (attributes) were employed to create a Multi-tree XGBoost-based model for an earlier prediction of SARS-CoV-2. Overall, our Multi-tree XGBoost model predicted SARS-CoV-2 infection status with a high F1-score (0.9920 $\pm \boldsymbol{0.008)}$ and AUC value (0. 9974 ± 0.0026) only by assessing the primary three clinical symptoms related to COVID-19 infection. Thus our multi-tree XGBoost - based model suggests a simple and accurate method for earlier detection of SARS-CoV-2 cases and initiating proper treatment protocol for SARS-CoV-2 positive patients. Therefore, we can conclude that our model will allow the health organizations to potentially reduce the infection rate and mortality in masses with COVID-19 infection and fatality due to SARS-CoV-2.