## 2 Related Works  
In the study by [PERSON] et al. [5], liver disease prediction was carried out using machine learning (ML) algorithms. The Indian Liver Patient Dataset (ILPD) was used as the dataset. The ML algorithms employed in the study included Decision Tree (DT), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Pure Bayes (Naive Bayes--NB), Logistic Regression (LR), and Random Forest (RF). Accuracy, precision, sensitivity, and specificity were used as performance criteria for these algorithms. The average success rates for these criteria were as follows: specificity (98.6%), accuracy (96.55%), precision (93.698%), and sensitivity (92.1%). [PERSON] et al. [6] conducted a study on the presence of liver fibrosis and the estimation of its degree in patients with hepatitis C virus. The study analyzed data from 100 Egyptian patients with hepatitis C virus. A machine learning technique based on a Decision Tree (DT) classifier was employed. The classification achieved an accuracy rate of 93.7%. [PERSON] et al. [7] performed a study utilizing the ILPD dataset and employed two algorithms, Boosted C5.0 and CHAID, for analysis. Comparison of the performance of these algorithms revealed that the Boosted C5.0 algorithm achieved an accuracy of 93.75%, indicating superior performance compared to the CHAID algorithm, which exhibited an accuracy of 65.00%. [PERSON] et al. [8] aimed to compare the performance of traditional statistical models with machine learning approaches to predict survival after liver transplants. In their study, five machine learning methods and four traditional statistical models were compared. The AUC-ROC value was calculated as 0.86 for the 3 months. [PERSON] et al. [9] estimated the probability that a liver from a donor would belong to the survival graft class after one year of transplantation with a decision support system. In this study, a feed-forward neural network with generalized radial basis functions in the hidden layer was used. [PERSON] et al. [10] conducted a study on the diagnosis of the most common type of hepatitis C disease in the liver using machine learning algorithms. Data were collected by experts from Cairo University and Kasr Al-Aini Hospital in Egypt. Linear Regression, Alternative Decision Tree, Classification and Regression Trees (CART), and REPTree classifier algorithms were employed. The performance of the classifiers was assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and AUC-ROC. The alternative decision tree algorithm achieved the highest accuracy of 95.6% and the highest AUC value of 99%. In the [PERSON] et al. study [11], an ANN was used for donor-recipient matching in liver transplantation and its accuracy was compared with validated scores. The best performance was obtained with multiple regression in estimating the probability of graft-survival (90.79%) and loss (71.42%) for each donor-recipient match. In the [PERSON] et al. study [12], postoperative sepsis was predicted in liver transplant patients using machine learning algorithms. The random forest classifier model estimated 0.731 AUC with 71.6% accuracy. [PERSON] conducted a study on hepatitis diagnosis using recurrent neural networks. The dataset was categorized into four classes: blood donor, hepatitis, fibrosis, and cirrhosis. Models such as Feed-forward Neural Network (FFNN), Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were employed. Performance evaluation criteria included accuracy, precision, recall, and the F1-score. Among these models, Simple RNN achieved the best performance with an accuracy of 97.72%. Additionally, the Simple RNN model exhibited the highest F1-score for the classification of all diseases [13]. Various studies in the literature have pursued the same objective using different methods. Different machine learning (ML) and deep learning (DL) algorithms have been employed to enhance the performance of the models. [PERSON] et al.