## 4 Results and Discussion
### Examining the Relevance of Extracted Key Gens Using Conventional Classification & Data Augmentation  
The key genes expression matrix has been augmented using the sparse autoencoder to generate more samples, as we have an insufficient number of samples. The augmented data has been applied to the classification of HCV-infected and disinfected samples Each autoencoder consists of an encoder/decoder module with one hidden layer in each module. The training procedure is built on optimizing the cost formula, which calculates the error between the input data X and its regenerated output data, X', on each iteration. The mean square reconstruction error of autoencoder has been calculated for the generated samples, as illustrated by Table 5. The effectiveness of the generated feature matrices has been investigated by comparing their performance in classifying the HCV-infected and disinfected samples. The following conventional supervised classifiers, including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN), have been extensively tested. In KNN, three values of K have been tested, including 1, 3, and 5. In SVM, three kernel functions have been employed, including linear, polynomial, and the Radial Base Kernel Function (RBF). Two polynomial orders (2, and 3) has been applied for the polynomial kernel function. Standardized and optimized RBF have been employed. All classifiers have been trained using 10-fold cross-validation to prevent over-fitting. During each fold, the learning model has been trained on nine divisions and verified on the 10 th. The confusion matrix has been calculated in each fold and a summarized one was used to calculate the accuracy. Table 6 illustrates the classification accuracy for all feature selection methods discussed here. The highest accuracies are highlighted in a grey color. The retrieved key genes using T&F test have yielded the highest classification accuracy, 95.83%, using the QDA classifier. The extracted key genes, using PCA test, have returned a classification accuracy of 93.75% using the QDA classifier. On the other side, the key genes retrieved using PCA, and Euclidean distance have returned a 91.67% classification accuracy using the SVM classifier.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Selection Method** & **Mean Squared Reconstruction Error of the** \\  & **Generated Samples** \\ \hline T, and F test & 0.0891 \\ \hline Pearson’s correlation, and cosine coefficient & 0.04966 \\ \hline Euclidean distance & 0.05345 \\ \hline Principal component analysis (PCA) & 0.005861 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mean squared reconstruction error of the generated samples using sparse autoencoder.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Feature Selection** & \multicolumn{2}{c}{**LDA-Linear**} & \multicolumn{2}{c}{**QQA-Quadratic**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-11}
**Method** & & & **K = 1** & **K = 3** & **K = 5** & \begin{tabular}{c} **SVM-** \\ **Linear** \\ \end{tabular} & \begin{tabular}{c} **SVM-** \\ **Quadratic** \\ \end{tabular} & \begin{tabular}{c} **SVM-** \\ **Cubic** \\ \end{tabular} & \
\begin{tabular}{c} **SVM-** \\ **RBF** \\ \end{tabular} &
\begin{tabular}{c} **SVM-** \\ **Optimized** \\ \end{tabular} \\ \hline T and F test & 66.7 & 95.83 & 79.1667 & 68.75 & 60.417 & 37.50 & 66.667 & 79.167 & 56.25 & 83.334 \\ \hline [PERSON]’s correlation coefficient & & & & & & & & & \\ \hline Euclidean distance & 69.75 & 78.2 & 81.25 & 79.167 & 77.083 & 50 & 81.25 & 85.417 & 81.25 & 91.667 \\ \hline Principal component analysis (PCA) & 70.8 & 93.75 & 85.41667 & 70.8334 & 72.9167 & 50 & 89.5833 & 87.50 & 77.083 & 91.667 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The classification accuracies based on the generated samples of the extracted key genes.