Classification of COVID19 Patients Using Robust Logistic Regression



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Ghosh, Abhik and Jaenada Malagón, María and Pardo Llorente, Leandro (2022) Classification of COVID19 Patients Using Robust Logistic Regression. Journal of Statistical Theory and Practice, 16 (4). ISSN 1559-8608

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Coronavirus disease 2019 (COVID19) has triggered a global pandemic affecting millions of people. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the COVID-19 disease is hypothesized to gain entry into humans via the airway epithelium, where it initiates a host response. The expression levels of genes at the upper airway that interact with the SARS-CoV-2 could be a telltale sign of virus infection. However, gene expression data have been flagged as suspicious of containing different contamination errors via techniques for extracting such information, and clinical diagnosis may contain labelling errors due to the specificity and sensitivity of diagnostic tests. We propose to fit the regularized logistic regression model as a classifier for COVID-19 diagnosis, which simultaneously identifies genes related to the disease and predicts the COVID-19 cases based on the expression values of the selected genes. We apply a robust estimating methods based on the density power divergence to obtain stable results ignoring the effects of contamination or labelling errors in the data and compare its performance with respect to the classical maximum likelihood estimator with different penalties, including the LASSO and the general adaptive LASSO penalties.

Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2022)

Uncontrolled Keywords:Density power divergence; High-dimensional data; Sparse logistic regression; COVID-19; Gene expression
Subjects:Sciences > Mathematics > Mathematical statistics
Medical sciences > Medicine > Communicable diseases
Medical sciences > Medicine > Medical genetics
Medical sciences > Biology > Biomathematics
ID Code:74726
Deposited On:26 Sep 2022 11:03
Last Modified:26 Sep 2022 14:08

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