The effect of well-known burn-related features on machine learning algorithms in burn patients' mortality prediction

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dc.authoridYazici, Hilmi/0000-0001-7470-0518
dc.authoridAygul, Yesim/0000-0003-0605-9604
dc.authorwosidYazici, Hilmi/AFK-1870-2022
dc.contributor.authorYazici, Hilmi
dc.contributor.authorUgurlu, Onur
dc.contributor.authorAygul, Yesim
dc.contributor.authorYildirim, Mehmet
dc.contributor.authorUcar, Ahmet Deniz
dc.date.accessioned2024-03-09T18:48:43Z
dc.date.available2024-03-09T18:48:43Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractBACKGROUND: Burns is one of the most common traumas worldwide. Severely injured burn patients have an increased risk for mortality and morbidity. This study aimed to evaluate well-known risk factors for burn mortality and comparison of six machine learning (ML) Algorithms' predictive performances. METHODS: The medical records of patients who had burn injuries treated at Izmir Bozyaka Training and Research Hospital's Burn Treatment Center were examined retrospectively. Patients' demographics such as age and gender, total burned surface area (TBSA), Inhalation injury (II), full-thickness burns (FTBSA), and burn types (BT) were recorded and used as input features in ML models. Patients were analyzed under two groups: Survivors and Non-Survivors. Six ML algorithms, including k-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Multi-Layer Perceptron, and AdaBoost (AB), were used for predicting mortality. Several different input feature combinations were evaluated for each algorithm. RESULTS: The number of eligible patients was 363. All six parameters (TBSA, Gender, FTBSA, II, Age, BT) that were included in ML algorithms showed a significant difference (p<0.001). The results show that AB algorithm using all input features had the best prediction performance with an accuracy of 90% and an area under the curve of 92%. CONCLUSION: ML algorithms showed strong predictive performance in burn mortality. The development of an ML algorithm with the right input features could be useful in the clinical practice. Further investigations are needed on this topic.en_US
dc.identifier.doi10.14744/tjtes.2023.79968
dc.identifier.endpage1137en_US
dc.identifier.issn1306-696X
dc.identifier.issn1307-7945
dc.identifier.issue10en_US
dc.identifier.pmid37791433en_US
dc.identifier.scopus2-s2.0-85173024973en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1130en_US
dc.identifier.urihttps://doi.org/10.14744/tjtes.2023.79968
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1432
dc.identifier.volume29en_US
dc.identifier.wosWOS:001087012200010en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTurkish Assoc Trauma Emergency Surgeryen_US
dc.relation.ispartofUlusal Travma Ve Acil Cerrahi Dergisi-Turkish Journal of Trauma & Emergency Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBurn; Machine Learning; Mortality; Predictionen_US
dc.titleThe effect of well-known burn-related features on machine learning algorithms in burn patients' mortality predictionen_US
dc.typeArticleen_US

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