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A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients


The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.

Type Journal
ISBN 0957-4174 (Print) 0957-4174 (Linking)
Authors Ahamad, M. M.; Aktar, S.; Rashed-Al-Mahfuz, M.; Uddin, S.; Lio, P.; Xu, H.; Summers, M. A.; Quinn, J. M. W.; Moni, M. A.
Responsible Garvan Author Dr Mohammad Ali Moni
Published Date 2020-12-01
Published Volume 160
Published Pages 113661
Status Published in-print
DOI 10.1016/j.eswa.2020.113661
URL link to publisher's version