The neural networks, naive Bayes, decision tree, SVM, random forest, and Bayesian network have been used in [8] for early prediction of cirrhotic patients based on the Egyptian dataset. Among all these ML algorithms the Bayesian network algorithm achieved the highest performance (AUC = 74.8% and accuracy 68.9%). The Intelligence Hepatitis C Stage System (IHSDS) with ANN was used to predict the stage of hepatitis C in [9]. The model achieved 94.9% classification accuracy. The decision tree, genetic algorithm (GA) particle swarm optimization, and multi-linear regression models were developed and used in [10] for predictions of advanced fibrosis by combining the serum biomarkers and clinical information to develop the classification models. The results of the study showed that machine learning (ML) algorithms were able to predict advanced fibrosis in patients with AUCROC in the range 0.73-0.76 and with an accuracy of 66.3-84.4%,respectively. The CatBoost, XGBoost, RFGini, LightGBM, Random forest (RF), and KNN have been used in [11] to detect Hepatitis C patients The result showed that of all ML modes highest accuracy (0.9593), recall (0.6667), precision (1), and F1-score (0.7867) was achieved with XGBoost algorithm. In [12] the supervised learning (decision tree, logistic regression, KNN, Extreme Gradient Boosting, Gradient Boosting Machine, Gaussian Naive Bayes, RF, Gradient Boosting, SVM), and unsupervised learning (K-means, Hierarchical clustering, DBMSCN, Gaussian Mixture, and K-means) models were used to detect the Hepatitis C virus from a dataset containing laboratory data of Hepatitis C patients and blood donors. The results showed that Logistic Regression and Gaussian Mixture models achieved the best accuracy score which is equivalent to 0.943 and the mutual information score of 0.9771, respectively. The Hepatitis C patient's outcome was investigated in [13] using classification techniques such as Logistic Regression, Decision Tree, SVM, and Naive Bayes. The results of this investigation showed that the highest accuracy (87.17%) was achieved using SVM. The ML models (Logistic Regression, Naive Bayes, Decision Tree, Random Forest, Extreme Gradient Boosting, kNN, SVM, ANN, and Ensemble methods) were built in [14] to predict the extent of fibrosis in patients with chronic Hepatitis C. Among all ML algorithms, the XGB achieved the highest accuracy (0.84), specificity (0.95), and specificity (0.73). There is some notable research in which AI and ML algorithms have been used in the detection of liver cancer. In [15] the authors have used the support vector machines method for identifying the liver cancer tumor for ultrasound images. The results showed that using this method a classification accuracy of 96.72% was achieved. The ANN and logistic regression have been used in [16] to develop a model for predicting the development of liver cancer within 6 years of diagnosis with type II diabetes. The best results were achieved with ANN in terms of sensitivity (75.7%), specificity (75.5%), and the area under the receiver operating characteristic curve (87.3%). The ANN and classification of regression tree have been used in [17] on a dataset collected from the cancer registration database in Northern Taiwan medical center from 2004 to 2008 to predict the survival of patients with liver cancer. The best results were achieved with ANN in terms of accuracy (87%), sensitivity (88%), specificity (87%), and area under the receiver operating characteristic curve (91.5%). The ensemble method has been developed and used in [18] to predict liver cancer in patients based on DNA sequence. Initially, the Naive Bayes, (GLM), kNN, SVM and C5.0 Decision Tree have been considered as elements of the ensemble method however, the best results were achieved with the ensemble method consisting of C5.0 Decision Tree, kNN, and SVM. With this ensemble method the achieved accuracy, sensitivity, and specificity in prediction of the liver cancer are 88.4%, 88.4%, and 91.6%, respectively.