XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study

Among the evaluated algorithms, the XGBoost model demonstrated superior performance, achieving an AUROC of 98.60% in cross-validation and 97.50% in the test set, indicating the strongest performance metrics within internal validation.

Authors

Devrim Bozkurt (ORCID: https://orcid.org/0000-0003-4557-3390)
Kamil Gönderen
Nur SOYER
Caner Acar (ORCID: https://orcid.org/0000-0002-9782-6807)
Evrim Özmen
Sukriye Miray Kilincer Bozgul
Busra Emir
Mehmet Göktuğ Efgan

Institutions

Izmir Kâtip Çelebi University (TR)
Ege University (TR)

Publication Details

Journal: Frontiers in Medicine
Published: 2026-02-09
DOI: https://doi.org/10.3389/fmed.2026.1715551
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study

Devrim Bozkurt, Kamil Gönderen, Nur SOYER, Caner Acar et al.

Feb 9, 2026

Frontiers in Medicine

Sepsis Diagnosis and Treatment

article

XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study

Devrim Bozkurt, Kamil Gönderen, Nur SOYER, Caner Acar, Evrim Özmen, Sukriye Miray Kilincer Bozgul, Busra Emir, Mehmet Göktuğ Efgan

February 9, 2026article en

Abstract

Among the evaluated algorithms, the XGBoost model demonstrated superior performance, achieving an AUROC of 98.60% in cross-validation and 97.50% in the test set, indicating the strongest performance metrics within internal validation.

Frontiers in MedicineVol. 13

Izmir Kâtip Çelebi University (TR), Ege University (TR)

Openalex Percentile: Top 61%

Sepsis Diagnosis and Treatment

DOI Open Access PDF OpenAlex

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