Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers
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 pa...
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MDPI AG
2025-01-01
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| author | Mete Yağanoğlu Gürkan Öztürk Ferhat Bozkurt Zeynep Bilen Zühal Yetiş Demir Sinan Kul Emrah Şimşek Salih Kara Hakan Eygu Necip Altundaş Nurhak Aksungur Ercan Korkut Mehmet Sinan Başar Nurinnisa Öztürk |
| author_facet | Mete Yağanoğlu Gürkan Öztürk Ferhat Bozkurt Zeynep Bilen Zühal Yetiş Demir Sinan Kul Emrah Şimşek Salih Kara Hakan Eygu Necip Altundaş Nurhak Aksungur Ercan Korkut Mehmet Sinan Başar Nurinnisa Öztürk |
| author_sort | Mete Yağanoğlu |
| collection | DOAJ |
| description | 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 Atatürk 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. |
| format | Article |
| id | doaj-art-41b3395c355b4b2fb8a898a118f7611b |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-41b3395c355b4b2fb8a898a118f7611b2025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153124810.3390/app15031248Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant CentersMete Yağanoğlu0Gürkan Öztürk1Ferhat Bozkurt2Zeynep Bilen3Zühal Yetiş Demir4Sinan Kul5Emrah Şimşek6Salih Kara7Hakan Eygu8Necip Altundaş9Nurhak Aksungur10Ercan Korkut11Mehmet Sinan Başar12Nurinnisa Öztürk13Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum 25240, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum 25240, TurkeyDepartment of Computer Technologies, Vocational School of Technical Sciences, Bayburt University, Bayburt 69000, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyFaculty of Open and Distance Education, Ataturk University, Erzurum 25240, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Erzurum Technical University, Erzurum 25050, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyDepartment of Statistics, Faculty of Economics and Administrative Sciences, Ataturk University, Erzurum 25240, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyAtaturk University Organ Transplant Center, Ataturk University, Erzurum 25240, TurkeyFaculty of Open and Distance Education, Ataturk University, Erzurum 25240, TurkeyDepartment of Medical Biochemistry, Faculty of Medicine, Ataturk University, Erzurum 25240, TurkeyThe 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 Atatürk 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.https://www.mdpi.com/2076-3417/15/3/1248deep learninglivermachine learningtransplantation |
| spellingShingle | Mete Yağanoğlu Gürkan Öztürk Ferhat Bozkurt Zeynep Bilen Zühal Yetiş Demir Sinan Kul Emrah Şimşek Salih Kara Hakan Eygu Necip Altundaş Nurhak Aksungur Ercan Korkut Mehmet Sinan Başar Nurinnisa Öztürk Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers Applied Sciences deep learning liver machine learning transplantation |
| title | Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers |
| title_full | Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers |
| title_fullStr | Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers |
| title_full_unstemmed | Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers |
| title_short | Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers |
| title_sort | development of a clinical decision support system using artificial intelligence methods for liver transplant centers |
| topic | deep learning liver machine learning transplantation |
| url | https://www.mdpi.com/2076-3417/15/3/1248 |
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