<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-18T23:16:15Z</responseDate><request verb="GetRecord" identifier="oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/12409" metadataPrefix="oai_dc">https://ebyu-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/12409</identifier><datestamp>2026-10-18T21:13:21Z</datestamp><setSpec>openaire</setSpec><setSpec>driver</setSpec><setSpec>com_ebyu_10000</setSpec><setSpec>col_ebyu_10001</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees</dc:title><dc:creator>Huyut, Mehmet Tahir</dc:creator><dc:creator>Huyut, Zübeyir</dc:creator><dc:subject>Artificial intelligence</dc:subject><dc:subject>CHAID decision Trees</dc:subject><dc:subject>Coagulation tests</dc:subject><dc:subject>COVID-19</dc:subject><dc:subject>Ferritin</dc:subject><dc:subject>Immunological tests</dc:subject><dc:subject>Machine learning</dc:subject><dc:subject>Mortality risk biomarkers</dc:subject><dc:description>Background and objective: A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method: This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results: Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions: This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality. © 2023 The Authors</dc:description><dc:publisher>Elsevier Ltd</dc:publisher><dc:date>2023</dc:date><dc:type>Article</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/ebyu/12409</dc:identifier><dc:identifier>https://doi.org/10.1016/j.heliyon.2023.e14015</dc:identifier><dc:identifier>2405-8440</dc:identifier><dc:language>en</dc:language><dc:relation>Heliyon</dc:relation><dc:rights>info:eu-repo/semantics/openAccess</dc:rights><dc:type>info:eu-repo/semantics/article</dc:type><dc:identifier>https://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12409</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>