{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T07:23:08Z","timestamp":1772349788902,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of identifying the most informative prognostic markers. For Machine Learning (ML) analysis, we utilized a dataset comprising 226 observations with 68 clinical, biochemical, and genetic features collected from 226 patients with confirmed COVID-19 (54\u2014moderate, 142\u2014severe and 30 with mild disease). The target variable was disease severity (mild, moderate, severe). The feature set included demographic variables (age, sex), genetic markers (single-nucleotide polymorphisms (SNPs) in FGB (rs1800790), NOS3 (rs2070744), and TMPRSS2 (rs12329760)), biochemical indicators (IL-6, endothelin-1, D-dimer, fibrinogen, among others), and clinical parameters (blood pressure, body mass index, comorbidities). The target variable was disease severity. To identify the most effective predictive models for COVID-19 severity, we systematically evaluated multiple supervised learning algorithms, including logistic regression, k-nearest neighbors, decision trees, random forest, gradient boosting, bagging, na\u00efve Bayes, and support vector machines. Model performance was assessed using accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Among the predictors, IL-6, presence of depression\/pneumonia, LDL cholesterol, AST, platelet count, lymphocyte count, and ALT showed the strongest correlations with severity. The highest predictive accuracy, with negligible error rates, was achieved by ensemble-based models such as ExtraTreesClassifier, HistGradientBoostingClassifier, BaggingClassifier, and GradientBoostingClassifier. Notably, decision tree models demonstrated high classification precision at terminal nodes, many of which yielded a 100% probability for a specific severity class.<\/jats:p>","DOI":"10.3390\/computation13100238","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:54:31Z","timestamp":1760028871000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Management of Severe COVID-19 Diagnosis Using Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9279-9531","authenticated-orcid":false,"given":"Larysa","family":"Sydorchuk","sequence":"first","affiliation":[{"name":"Department of Family Medicine, Bukovinian State Medical University, 58012 Chernivtsi, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7150-7146","authenticated-orcid":false,"given":"Maksym","family":"Sokolenko","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases and Epidemiology, Bukovinian State Medical University, 58012 Chernivtsi, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6658-2742","authenticated-orcid":false,"given":"Miroslav","family":"\u0160koda","sequence":"additional","affiliation":[{"name":"Department of Management and Accounting, DTI University, 533\/20, 018 41 Dubnica nad V\u00e1hom, Slovakia"}]},{"given":"Daniel","family":"Lajcin","sequence":"additional","affiliation":[{"name":"Department of Management and Accounting, DTI University, 533\/20, 018 41 Dubnica nad V\u00e1hom, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4766-4659","authenticated-orcid":false,"given":"Yaroslav","family":"Vyklyuk","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79005 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3603-3432","authenticated-orcid":false,"given":"Ruslan","family":"Sydorchuk","sequence":"additional","affiliation":[{"name":"Department of General Surgery \u21162, Bukovinian State Medical University, 58012 Chernivtsi, Ukraine"}]},{"given":"Alina","family":"Sokolenko","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, Bukovinian State Medical University, 58012 Chernivtsi, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3919-4412","authenticated-orcid":false,"given":"Dmytro","family":"Martjanov","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79005 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1038\/s41579-024-01036-y","article-title":"COVID-19 drug discovery and treatment options","volume":"22","author":"Chan","year":"2024","journal-title":"Nat. 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