AUTHOR=Pisano Fabio , Cannas Barbara , Fanni Alessandra , Pasella Manuela , Canetto Beatrice , Giglio Sabrina Rita , Mocci Stefano , Chessa Luchino , Perra Andrea , Littera Roberto 

TITLE=Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1230733

DOI=10.3389/fmed.2023.1230733

ISSN=2296-858X

ABSTRACT=Few Artificial Intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82% and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Further studies are required for routine clinical integration.