###### Abstract  
Cancer diagnosis and treatment depend on accurate cancer-type prediction. A prediction model can infer significant cancer features (genes). Gene expression is among the most frequently used features in cancer detection. Deep Learning (DL) architectures, which demonstrate cutting-edge performance in many disciplines, are not appropriate for the gene expression data since it contains a few samples with thousands of features. This study presents an approach that applies three feature selection techniques (Lasso, Random Forest, and Chi-Square) on gene expression data obtained from Pan-Cancer Atlas through the TCGA Firehose Data using R statistical software version 4.2.2. 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. We constructed five models with a simple convolutional neural networks (CNNs) architecture, which are trained using the selected features and then selected the winning model. The winning model achieved a precision of 94.11%, a recall of 94.26%, an F1-score of 94.14%, and an accuracy of 96.16% on a test set. cancer prediction; gene expression; deep learning; Pan-Cancer Atlas; convolutional neural networks +
Footnote †: journal:  
Due to the accessibility of publicly available cancer data over the past ten years, traditional machine learning approaches have been developed [5; 6; 7; 8; 9; 10]. On the other hand, a set of neural network models with multi-layers called deep learning (DL) excels at the challenge of being trained with large amounts of data. Like traditional machine learning techniques, DL entails two steps: training, which involves estimating the parameters of the network from a specified dataset known as the training set, and testing, which uses a testing set to evaluate the learned network performance. The development of deep learning approaches that have innovative interpretability and high accuracy in predicting the types of cancers was made possible by accumulating whole transcriptome profiling of tumor data. One of these profiling data is the Cancer Genome Atlas (TCGA), a well-known cancer transcriptome profiling database containing the 33 most common types of cancer [11]. Many models based on different DL are created for the detection and classification of cancer. Research that utilized multiple models based on convolutional neural networks (CNNs) built for various input data types was reported by [PERSON] et al. in their publication [12]. These models rigorously examine the ability of the convolution kernels. [PERSON] et al. assessed the performance of their models in predicting tumor types using the TCGA data, which contains the gene expression of 33 types of cancer. Their models achieved prediction accuracies ranging from 93.9% to 95%. Four Graph CNNs models were suggested and trained by [PERSON] et al. [13] utilizing the whole TCGA gene expression data sets to classify 33 different cancer types. The models had prediction accuracies from 89.9 to 94.7%. [PERSON] et al. [14] developed a CNNs model and obtained more than 95% classification accuracy for 33 cancer types retrieved from TCGA. They mapped the gene expression samples into two-dimensional matrices for input. [PERSON] et al. [15] presented a CNNs approach for the classification of seven types of cancer retrieved from the TCGA dataset and obtained an overall accuracy of 77.6%. Our group [16] proposed five 1D-CNN-based stacking ensemble approaches for classifying the most malignancies affecting women. The developed model uses RNASeq data obtained from TCGA as an input. The output of these models is integrated using Neural Network (NN), which is then utilized as a meta-model. [PERSON] et al. [17] assessed the application of five different machine learning (random forest, GBM, REFRN, SVM, and KNN) with RNASeq data from 17 cancer types. In a recent study, researchers compared the performance of different machine learning algorithms for cancer research.