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. 2023 May 22;14:1155624. doi: 10.3389/fmicb.2023.1155624

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Copyright © 2023 Petrone, Fanelli, Giudice, Toschi, Conti, Maracchioni, Iannetta, Resta, Cipriani, Miele, Amati, Andreoni, Sarmati, Rogliani, Novelli, Garaci, Rasi, Sinibaldi-Vallebona, Minutolo, Matteucci, Balestrieri and Grelli.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Results of the machine learning model (hospitalized vs not hospitalized SARS-CoV-2-positive). Based on the results of factor analysis. (A) Cumulative variance and single loadings computed through factor analysis; (B) feature importance ranking derived through SHAP values; (C) SHAP dependence plots for each component, showing median and confidence intervals across 100 repetitions, in black and violet, respectively. Summary of classification performance: AUC 0.85 ± 0.12; Accuracy 0.90 ± 0.08; Sensitivity 0.98 ± 0.05; Specificity 0.73 ± 0.22; f1-score 0.80 ± 0.18; PPV 0.94 ± 0.13; NPV 0.90 ± 0.08.