A Case Study of Using Machine Learning Techniques for COVID-19 Diagnosis

The Coronavirus disease (COVID-19) is a worldwide pandemic that has lead to millions of death and is affecting every corner of the society. The industrial and scientific communities are continuously working to curb the spread of the pandemic, with efforts in numerous areas including disease detection and diagnosis, virology, vaccine and drug development. As a powerful technique, Artificial Intelligence (AI) and machine learning techniques have been widely incorporated in COVID-19 related research and development. With the aim to establish a use case of machine learning techniques for COVID-19 diagnosis, this paper applies the XGBoost machine learning technique, while examining a number of hyperparameters and data preprocessing techniques, to identify an accurate predictive model, followed by the use of Shapley value to study predictors that are most informative of the diagnosis. Evaluated on a collection of anonymised patients data collected out of the standard Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) and additional laboratory test results, the best model obtained demonstrates high diagnostic performance.