## 2 Review of Literature
The authors of [16] introduced a feature selection strategy and a multigrain cascade forest classifier (gcForest) to predict four breast cancer subtypes. The modeling process employed RNA-Seq gene expression data from The Cancer Genome Atlas (TCGA). The proposed method aimed to enhance the classification accuracy and reduce the training time by selecting informative genes. To evaluate the effectiveness of gcForest, the classification results were compared with those of three well-known classifiers (KNN, SVM, and MLP). In the first stage of the study, the thirty most informative genes were selected. The modeling results revealed the higher efficiency of the gcForest algorithm compared to the other classifiers, with an accuracy of 0.92 achieved. However, a drawback of gcForest is that it requires discrete data, resulting in the loss of information and a decrease in data classification accuracy. Additionally, external data were not used to assess the adequacy of the proposed model in the presented studies. In recent years, the growth of computing resources and their application in processing gene expression data has led to the widespread use of artificial intelligence methods based on deep learning (DL) [17; 18; 19]. DL is a branch of machine learning that creates a structure with multiple levels, where the output of one level is the input of the next level. The DL network structure aims to emulate the mechanisms of the human brain in interpreting different types of data, such as sound, text, and images. DL uses principles similar to linear regression, where each neuron has a weighted value updated using the backpropagation algorithm, to minimize the global loss function. The application of DL has helped to overcome challenges in predicting cancer, by providing high accuracy in disease identification and faster analysis. The most common architectures of deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and artificial neural networks (ANNs). In [20], the authors presented the results of their research on early cancer diagnosis using a deep neural network (DNN). The proposed model was tested on 37 different types of cancer using data from The Cancer Genome Atlas (TCGA), which contained the expression of 10,000 genes in 10,663 samples (9807 tumors and 856 normal samples). The authors investigated three different structures of deep neural network model: 3 NN, 5 NN, and 9 NN. The performance of the proposed DNN model was compared to that of a support vector machine (SVM) model by calculating their accuracy of classifying the samples. The modeling results showed that the 5 NN model achieved the highest diagnostic accuracy. However, it is worth noting that the disease identification accuracy was not high, and the model's sensitivity to certain types of cancer was also limited, indicating that further refinement is necessary. The findings of a study on cancer diagnosis using an ensemble of deep learning models are presented in [21]. The authors investigated the use of gene expression data from The Cancer Genome Atlas (TCGA) to diagnose lung, breast, and stomach cancers. The study included 162 lung cancer samples, 878 breast cancer samples, and 271 stomach cancer samples. The model achieved a diagnostic accuracy of 98% for all datasets. Despite this impressive result, there are significant drawbacks to the proposed model. First, it is highly complex and requires substantial time and computational resources for training. Second, interpreting this ensemble-based deep learning model is challenging. After analyzing the research in this area, it can be concluded that there is currently no definitive solution to the problem of high-quality processing of gene expression data, for creating a diagnostic system for various diseases. However, we believe that this problem can be solved by employing a complex approach that combines modern methods of intelligent data analysis and machine learning with advanced information technologies for processing big data.