## 4 Applying Convolutional Neural Network (CNN) to Gene Expression Data in Formed Clusters
If the classifier identifies different patient states based on gene expression data from different clusters, the patient's condition is identified as undefined, indicating the need for further clinical studies. It should be noted that increasing the number of clusters contributes to increasing the objectivity of making an adequate decision regarding the patient's health status, by parallelizing the process of processing gene expression data, on the one hand, and increasing the number of possible alternatives, on the other. Practical Implementation of the Binary Classification Model of Patient Health Status Based on a Multi-Cluster Structure of Gene Expression Data Using the Alternative Voting Method  
The results of the binary classification of samples containing gene expression data using different cluster structures are presented in Figure 7.  
The random forest (RF) classification algorithm was used at this stage. The feasibility of using this type of algorithm was confirmed by the fact that the initial data were divided into subgroups during binary classification, and the number of samples in each group (considering the type of disease) was significantly less than the total number of samples. For such limited data, using a convolutional neural network is not reasonable. In addition, the feasibility of using the RF algorithm for the binary classification of samples based on gene expression data was demonstrated in [27]. The simulation procedure was carried out with the following steps:  
1. Formation of subsets of data, with samples corresponding to one type for each cluster structure. At this stage, nine subsets were formed, eight of which corresponded to different types of cancer, and the ninth subset contained gene expression data of patients for whom no disease was detected. 2. Combination of subsets of gene expression data corresponding to different types of disease with the gene expression data of healthy patients. At this stage, eight subsets of gene expression data were formed for each cluster structure. Figure 7: Simulation results regarding the use of the random forest (RF) algorithm for binary classification of samples containing gene expression data from different cluster structures. 3. Division of each data group into two subsets in a ratio of 0.65/0.35, with the first being used for model training and the second for model testing. 4. Training the model by applying the RF algorithm to the first subset of data. 5. Application of the model to test data. Calculation of binary classification quality criteria. 6. Analysis of the obtained results. The analysis of the obtained results allowed us to conclude that in all cases, the accuracy of binary classification of samples was quite high (ranging from 98 to 100 percent). More detailed analysis showed the higher effectiveness of the model when applying four- and six-cluster structures, which was consistent with the results of evaluating the quality of cluster structure based on the use of internal and external criteria for clustering quality. Analysis of the simulation results also allowed us to conclude that the best results were obtained when applying the six-cluster structure, which confirmed the feasibility of using the model to parallelize the gene expression data processing flow and to make a final decision on the object's status by applying the alternative voting method. In this case, the importance of the previous stage of removing noninformative expression profiles based on statistical and entropy criteria and forming clusters of mutually correlated and differentially expressed gene expression profiles should be noted.