Open AccessA machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data
Author(s) -
Suzan FarhangSardroodi,
Mohammad Sajjad Ghaemi,
Morgan Craig,
Hsu Kiang Ooi,
Jane M. Heffernan
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022272
Subject(s) - receiver operating characteristic , immune system , machine learning , artificial intelligence , viral load , covid-19 , immunology , feature selection , disease , computer science , medicine , virus , infectious disease (medical specialty)
Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = $ 91\% $ using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.