Clinical data-driven approach to identifying COVID-19 and influenza from a gradient-boosting model

Abstract

Corona Virus Disease 2019 (COVID-19) and influenza are both caused by viruses, seriously affect human health, and are highly infectious. However, because the clinical manifestations of these two groups of diseases have almost identical symptoms, separate Polymerase Chain Reaction (PCR) tests must be used for patients in each disease group. This study proposes an automatic data-processing model based on artificial intelligence and gradient boosting to identifying COVID-19 and influenza. The model can learn directly from raw data without the need for human input to delete empty data. Methodology and techniques operate in two stages: first, it evaluates and processes data to reduce the dataset’s complexity using the light gradient boosting machine (LightGBM); then, in the second stage, it builds a classification model for each disease group based on the extreme gradient boosting (XGBoost) method. The research tools showed that combining two gradient-boosting models both LightGBM and XGBoost to generate automatic COVID-19 and influenza classifiers from clinical data produced strong results and a superior performance versus one model alone, with an overall accuracy of over 99.96%. In the future, the developed model will enable patients to be diagnosed simply and accurately and thereby reduce countries’ testing costs for COVID-19 and similar pandemics that may arise.