Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers

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dc.authorid0000-0002-4104-2368
dc.authorid0000-0002-7824-756X
dc.authorid0000-0003-0088-5825
dc.authorid0000-0002-5165-638X
dc.authorid0000-0003-3045-169X
dc.authorid0000-0002-6741-6268
dc.authorid0000-0002-7746-2700
dc.contributor.authorYaganoglu, Mete
dc.contributor.authorOzturk, Gurkan
dc.contributor.authorBozkurt, Ferhat
dc.contributor.authorBilen, Zeynep
dc.contributor.authorDemir, Zuehal Yetis
dc.contributor.authorKul, Sinan
dc.contributor.authorOzturk, Nurinnisa
dc.date.accessioned2026-02-28T12:18:14Z
dc.date.available2026-02-28T12:18:14Z
dc.date.issued2025
dc.departmentBayburt Üniversitesi
dc.description.abstractThe objective of this study is to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation. To achieve this, an interface was developed to collect patient information from Atat & uuml;rk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation. The collected data were subsequently entered into a comprehensive database. Additionally, relevant patient information was obtained through the hospital's information processing system, which was used to create a data pool. The classification of data was based on four dependent variables, namely, the presence or absence of death (exitus), recurrence location, tumor recurrence, and cause of death. Techniques such as Principal Component Analysis and Linear Discriminant Analysis (LDA) were employed to enhance the performance of the models. Among the various methods employed, the LDA method consistently yielded superior results in terms of accuracy during k-fold cross-validation. Following k-fold cross-validation, the model achieved the highest accuracy of 98% for the dependent variable exitus. For the dependent variable recurrence location, the highest accuracy obtained after k-fold cross-validation was 91%. Furthermore, the highest accuracy of 99% was achieved for both the dependent variables tumor recurrence and cause of death after k-fold cross-validation.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK); [120E403]
dc.description.sponsorshipThis study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK). Grant No 120E403.
dc.identifier.doi10.3390/app15031248
dc.identifier.issn2076-3417
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85217759905
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app15031248
dc.identifier.urihttps://hdl.handle.net/20.500.12403/6178
dc.identifier.volume15
dc.identifier.wosWOS:001418468200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260218
dc.subjectdeep learning
dc.subjectliver
dc.subjectmachine learning
dc.subjecttransplantation
dc.titleDevelopment of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers
dc.typeArticle

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