A machine learning model to identify early stage symptoms of SARS-Cov-2 infected
patients
#MMPMID32834556
Ahamad MM
; Aktar S
; Rashed-Al-Mahfuz M
; Uddin S
; Liņ P
; Xu H
; Summers MA
; Quinn JMW
; Moni MA
Expert Syst Appl
2020[Dec]; 160
(?): 113661
PMID32834556
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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.
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