## 3 Proposed Approach  
Figure 2 depicts the complete framework of our proposed method. First, we divided the entire data into testing and training sets before processing it with feature selection algorithms to prevent data leakage and model overfitting. Then, we applied feature selection procedures to the training set. This way, we will ensure no information is shared between the training and testing sets when applying the features selection algorithm. Suppose feature selection is used to prepare the data, followed by model selection and training on the chosen features. In this case, the model will be given the training set as a  
Figure 1: Total samples number for each cancer type and normal cases. whole for making feature selection decisions. This could lead to models that are improved by the chosen features over other tested models appearing to have better results when they have biased results [25]. Three feature selection techniques are used. These techniques are Lasso, Random Forest, and Chi-Square. These feature selection techniques are essential because they can identify the most important features that strongly impact the target variable and remove the less important features. We calculated the feature importance of each selection method. Then we calculated the mean of the feature importance to determine the threshold for selecting the most relevant features, which are then reshaped into 2D-image-like data. The thresholds that we used in this research are \(\upmu\), 0.5 \(\upmu\), 2 \(\upmu\), 4 \(\upmu\), and 8 \(\upmu\) and they are used to create five classification models. These classification models are trained based on a 10-fold cross-validation approach. Lasso is a regularization technique that enhances the functionality of linear regression models. To achieve this, a penalty that promotes the coefficients of less important characteristics to be zero is added to the loss function [26]. This makes the model easier to understand and more straightforward. Both classification and regression issues can be solved with Lasso. When there are many features, and the target variable is only affected by a small number of features, Lasso performs well. A penalty factor determines the number of features that are maintained. Choosing the penalty factor using cross-validation increases the likelihood that the model will generalize well to new data sets. If we consider a multinomial response with more than two levels \((K>2)\), we can suppose that \(p_{\frac{1}{2}}(g_{i})=\Pr(C=c_{i}|g_{i})\), where \(c_{i}\in\{1,\ 2,\ 3,\ \dots,\ K\}\) represents the probability of  
Figure 2: Overall framework for the proposed method. observing \(i\)th response. The multinomial LASSO model's log-likelihood can be expressed as follows [27]:  
\[ \begin{array}{c}max\\ \left\{\beta_{0^{T}_{i}},\ \beta_{\ddagger}\right\}ss_{1}^{K}\in\mathbb{R}^{K(p+1)} \left[\frac{1}{N}\sum_{i=1}^{N}\log p_{c_{i}}(g_{i})-\ \lambda\sum_{\ddagger=1}^{K}P_{\alpha}\left(\beta_{\ddagger}\right)\right]\end{array}  \tag{1}\]  
The aforementioned log-likelihood can be optimized through a penalized approach. Therefore, the regularized log-likelihood in Equation (1) can be represented in more detailed as follow  
\[\ddagger\left(\left\{\beta_{0^{T}_{i}},\ \beta_{\ddagger}\right\}_{1}^{K} \right)=\ \frac{1}{N}\sum_{i=1}^{N}\left[\sum_{\ddagger=1}^{K}y_{i \ddagger}\left(\beta_{0^{T}_{i}}+\ g_{i}^{T}\beta_{\ddagger}\right)-\log \left(\sum_{\ddagger=1}^{K}e^{\beta_{0^{T}_{i}}+\ g_{i}^{T}\beta_{\ddagger}} \right)\right] \tag{2}\]  
\[P_{\alpha}(\beta)=(1-\alpha)\frac{1}{2}\left|\left|\beta\right|\right|_{L_{2}} ^{2}+\ \alpha\left|\left|\beta\right|\right|_{L_{1}}=\ \sum_{j=1}^{p}\left[\frac{1}{2}(1-\alpha)\beta_{j}^{2}+\ \alpha\left|\beta_{j}\right|\right] \tag{3}\]  
The target variable can be represented by a matrix \(Y\), where \(Y\) has a dimension of \(NxK\), and \(y_{i\ddagger}=I(c_{i}=\ddagger)\).