## 5 Conclusions and Future Work  
In this study, the ML and DL methods were employed to evaluate the diagnosis of liver diseases, quality of life, and death status of patients after liver transplantation. The dataset comprised information from 110 patients who underwent liver transplantation at Erzurum Ataturk University Training and Research Hospital's organ transplantation department. The dataset contained 188 attributes, including the age and gender of living and cadaver donors, the cause of death of cadavers, recipient's blood results, transport procedures, blood usage, and post-transplant infection occurrence. The collected data underwent preprocessing, which involved handling outliers, checking for empty data, and removing duplicate entries. Following preprocessing, the dataset was divided into training (80%) and testing (20%) sets. To enhance the model's performance, feature extraction methods such as PCA and LDA were employed. ML algorithms utilized in the study included NB, SVM, LR, RF, DT, and KNN, while DL algorithms consisted of RNN and LSTM. Various performance metrics, including precision, recall, F1-score, and accuracy, were employed to assess the models' performance. The highest accuracy values were obtained using k-fold cross-validation. For the dependent variable \"Ex\", applying PCA with k-fold cross-validation yielded LR, KNN, SVM, and NB models with the highest accuracy of 96%. Similarly, applying LDA with k-fold cross-validation resulted in LR, KNN, SVM, and NB models achieving the highest accuracy of 98%. Regarding the dependent variable \"relapse location\", applying PCA with k-fold cross-validation yielded LR, KNN, SVM, and NB models with the highest accuracy of 96%. With LDA and k-fold cross-validation, LR, DT, RF, and LSTM models achieved the highest accuracy of 98%. Concerning the dependent variable \"tumor recurrence\", applying PCA with k-fold cross-validation resulted in LR, KNN, SVM, RF, and NB models achieving the highest accuracy of 96%. Applying LDA with k-fold cross-validation yielded LR and DT models with the highest accuracy of 99%. For the dependent variable \"cause of death\", applying PCA with k-fold cross-validation yielded LR with the highest accuracy of 87%. Using LDA and k-fold cross-validation, LR and KNN models achieved the highest accuracy of 99%. This manuscript aims to explore how artificial intelligence and machine learning algorithms can be applied in liver transplantation processes and to provide a proof of concept in this area. While the technical aspects of the models used are important, our primary focus is on how these models can be integrated into clinical decision support systems and contribute to clinical processes. Figure 22: ROC curve of ML models (**a**) and ROC curve of DL models (**b**) of the cause of death dependent variable with LDA. Specifically, in the context of liver transplantation, the independent variables analyzed include key parameters influencing patient outcomes. The potential contributions of the artificial intelligence methods employed in this study to clinical practice include supporting physicians in decision-making, automating complex data analyses, and providing faster and more accurate insights for patient management. While the primary audience of this study is engineers rather than clinicians, we believe that our findings will guide the development of AI-based systems and their integration into clinical settings in the future. This study evaluated the factors influencing the quality of life for patients following liver transplantation by utilizing a novel dataset generated through the proposed models. Distinct from existing research, this work introduced a newly developed dataset, offering fresh insights to the field. Future research will focus on achieving improved outcomes by leveraging larger datasets and extending the study's scope to encompass other types of organ transplants. This study is proof of concept and continues with increasing patient numbers at the center where it is being conducted.