Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
deep learning, liver, machine learning, transplantation
Kaynak
Applied Sciences-Basel
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
15
Sayı
3
