| Download | - View final version: A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data (PDF, 12.2 MiB)
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| DOI | Resolve DOI: https://doi.org/10.3934/mbe.2022272 |
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| Author | Search for: Farhang-Sardroodi, SuzanORCID identifier: https://orcid.org/0000-0001-7654-8804; Search for: Ghaemi, Mohammad Sajjad1; Search for: Craig, MorganORCID identifier: https://orcid.org/0000-0003-4852-4770; Search for: Ooi, Hsu Kiang1ORCID identifier: https://orcid.org/0000-0001-7934-4866; Search for: Heffernan, Jane MORCID identifier: https://orcid.org/0000-0001-9502-1688 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | biological systems; mechanistic model; infectious disease; Influenza (flu); COVID-19; machine learning; classification; logistic regression; regularization; Lasso; Ridge; PLS-DA |
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| Abstract | 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. |
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| Publication date | 2022-04-06 |
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| Publisher | American Institute of Mathematical Sciences (AIMS) |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | bd24343e-6a6e-4b56-8a51-e7e9bc3b7eb1 |
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| Record created | 2022-04-22 |
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| Record modified | 2022-04-22 |
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