## 2 Literature Review  
The non-invasive detection of new significant genes for the replication of HCV and its outcomes, such as Hepatocellular carcinoma using machine learning techniques, has been recently addressed in a diverse array of studies. Studying the early stages of HCV infection and detecting the host genes involved in the HCV life cycle was discussed in [8]. They utilized the same dataset applied in this research. A simple statistical method, Analysis of Variance (ANOVA), has been applied. An average of the triplicate values was used to calculate fold change, and each value was assessed for its statistical significance. Host genes having a p-value less than 0.05 were considered significant genes. The retrieved genes are those ones that has an increase/decrease in their expression of at least 2-fold. The work done in [1] investigated the potential of Alpha Fetal Protein (AFP) as a well-known biomarker for HCC [19; 20]. LncRNA (long non-coding RNAs) microarray has been used. Significant genes were selected using two univariate methods (chi-square tests and t-test). T-test was used to compare variances in the lncRNA expression of plasma tissue of normal and up normal samples. They detected three genes that might be potential biomarkers for tumorigenesis prediction and two genes for metastasis prediction in the future. Another study has been done in [10] and they compared the univariate and multivariate gene selection methods via a range of classifiers based on a diverse type of cancers. They concluded that univariate gene selection paradigms yielded better results than the multivariate ones in five out of seven datasets. In the univariate method, they applied Pearson correlation, _t_-statistic or SNR, signal to noise ratio, and applied base pair selection, forward selection and recursive feature elimination in the multivariate method. In the study done in [21], the author proposed new technique called Stable Gene Selection (SGS) which selects significant genes for training a Support Vector Machine (SVM) classifier [22]. Key genes are selected using Bayesian [23] and Lasso [24]. Then, the selected genes are used to train the SVM classification algorithm to build a prediction model. The proposed method (SGS) has been applied on four datasets and it outperformed the existing gene selection methods. Another study [25] utilized perception tools to predict the up/down regulated genes in microarray samples. They proposed the Kernel PCA (KPCA) [26] and Biplot [27] to plot gene expression profiles. They applied the proposed method on three types of cancer including lymphoma, colon tumor, and leukemia cancer datasets. The proposed procedure starts with the SVD of preprocessed gene expression input matrix then takes the row of matrices as a set of observations to compute Kernel matrix. The nonlinear features are calculated using PCA on the Kernel matrix. Similar work using machine learning for the same medical domain, HCV, is the work done in [28]. They applied a hybrid machine learning paradigm for diagnosing hepatitis disease. Four stages are used in that work, including dimension reduction, clustering, feature selection, and classification. The dimension of the data was reduced using non-linear iterative partial least squares, then the self-organizing map was applied to cluster the similar data points, Classification and Regression Trees (CART) for selecting the significant features, and the ensemble classifier to predict the class (live or die). The work done in [29] investigated the serum miR-218 and its expression in patients with HCC, and analyzed its potential in the diagnosis and prognosis of HCC. They compared the expression of the serum level of miR-218 in healthy liver and HCC tissues to assess the relationship between its expression normal and tumor samples. The demonstrative estimation of serum miR-218 in HCC was additionally examined. This study gave profitable confirmation of the recognizable proof of the serum miR-218 as prognostic biomarker for HCC.