## 2 Review of Literature  
Many scientific works are currently devoted to applying intelligent data analysis and machine learning methods, including deep learning, for processing gene expression data to create diagnostic systems for various diseases. According to the findings in [10], the authors introduced a method called an extremal learning machine (ELM) for categorizing gene expression data. This approach employs correlation analysis to identify helpful gene expression profiles. The authors achieved a binary classification accuracy of 79%. The study used 60 central nervous system tumors (including other types of tumor). However, it should be noted that a small dataset was used in the experiment, which may have affected the adequacy of the obtained results. Moreover, the effectiveness of the proposed model was evaluated based on only one dataset, which also does not contribute to the adequacy of the results. The accuracy of the obtained results was also not high, which is undoubtedly one of the drawbacks of the presented research results. The research presented in [11] explored using five distinct machine learning techniques for classifying gene expression data: the random forest algorithm, support vector machine, naive Bayes classifier, C4.5 decision trees, and k-nearest neighbor method. The study employed gene expression data from 358 patients with varying degrees of somatic mutations, sourced from The Cancer Genome Atlas (TCGA) database, to predict breast cancer. Among the methods used, the random forest algorithm achieved the highest accuracy of 70%, while the other machine learning techniques were found to be less precise. In [12], the authors explored the classification of two lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), using classifiers based on the random forest algorithm and support vector machines (SVM). To identify informative genes, they employed Monte Carlo feature selection (MCSF) and incremental feature selection methods. The gene expression arrays used for modeling contained 20,502 genes from the Affymetrix GeneChip [13], sourced from the Gene Expression Omnibus (GEO GSE43580) database. These data comprised 77 AC lung cancer samples and 73 SCC lung cancer samples. The results indicated that SVM achieved a higher accuracy when using 1100 optimal features (genes) for classification, compared to the 43 informative features (genes) obtained through MCSF. The accuracy rates dropped from 0.96 to 0.86 with SVM and from 0.93 to 0.88 with the random forest method. These findings highlight the significance of selecting informative gene expression profiles. The findings of a study involving gene expression data obtained from lung adenocarcinoma samples, precisely 86 tumor samples and ten nontumor samples sourced from the Kent Ridge Biomedical Data Repository (available from [14]), are presented in [15]. The data comprised 7129 genes, and a subset of informative genes was formed using the mutual information estimation method. The model was trained using 70% of the samples, with the remaining 30% used for testing. Three binary classification methods were employed to detect the presence of tumors after identifying candidate genes directly related to lung cancer. Several selected genes were evaluated for their biological significance in lung cancer pathology, leading to the identification of six genes with high information gain that may be associated with lung cancer. These genes were then used as biomarkers for lung cancer classification in the proposed model using three classification methods: multi-layer perceptron (MLP), random sub-space (RSS), and sequential minimal optimization (SMO). The classification accuracy achieved with these methods was 87%, 68%, and 92%, respectively. However, it should be noted that the classification accuracy obtained in this study was also deemed unsatisfactory, which can be attributed to the model's inability to perfectly form subsets of informative gene expression profiles.