###### Abstract
Previous studies have been conducted using machine learning to classify tumor types based on RNA-Sequencing. For example, in [10], the authors proposed using a convolutional neural network, CNN, to classify breast cancer. They achieved an accuracy of 98.76% by selecting hyper parameters for the CNN model that would give the best performance for the proposed method. In [11], the authors converted RNA-Seq values into 2D images and used them to extract features to train a deep learning model. Another deep learning method was proposed in [12], in which the RNA-Seq values were also converted into 2D images. In [12], the authors used augmentation techniques to increase the dataset 5-fold. By applying the CNN model, an accuracy of 96.90% was achieved. In [13], the authors conducted an RNA-Seq analysis for tumor classification. The proposed analysis only focused on five target tumor types (prostate, lung, breast, kidney, and colon), but utilized a similar 60,483 RNA-Seq feature dataset. However, the obtained tumor sample set Bonat was unbalanced across the five tumor classes. Extreme unbalanced classes can result in a skewed classification accuracy in favor of the majority class. The authors applied a popular over-sampling technique called the Synthetic Minority Oversampling Technique (SMOTE) which creates \"synthetic\" examples rather than over-sampling with replacements. With the application of SMOTE, the tumor sample set was balanced with 1500 total samples (300 for each of the tumor types). They also employed dimensionality reduction, usingPrimary Component Analysis (PCA). For modeling [13] used a broad range of classification models for comparison. The authors of [14] conducted a study focused on classifying five subtypes of breast cancer (BRCA) using RNA-Seq and machine learning. They used a broad range of classification models for comparison, similar to five of our models. Their dataset consisted of 4731 samples \(\times\) 19,737 RNA-Seq features. They did not utilize any dimensionality reduction techniques, instead electing to train and test on the full feature set. In [15], the authors utilized a deep learning model in their classification of 33 tumor types. Their dataset consisted of 10,267 samples \(\times\) 20,531 RNA-Seq features. They normalized their RNA-Seq features during preprocessing. They also applied dimensionality reduction using a process similar to a high correlation filter and built a reduced gene feature set based on the top pairing of highly correlated RNA-Seq expressions to target tumor types. This process, they called Mutual information (MI), yielded a reduced feature set of 3600 features. Next, they converted the RNA-Seq feature set information into an image format for modeling utilizing a _Dense Network Convolutional Neural Network (DenseNetCAM)_ model. The authors of [16], in their RNA-Seq study, utilized the same dataset as in [15] minus two tumor types (Esophageal (ESCA) and stomach (STAD) cancer). The authors, however, did not employ any dimensionality reduction. They utilized a single classification model, \(k\)-_Nearest Neighbors_ (\(k=5\)). In [17], similar to [15], utilized two-dimensional image transformations of the RNA-Seq data to classify 33 tumor types. They also used the same 10,267 damples \(\times\) 60,484 RNA-Seq feature dataset. The author of [17] normalized the gene feature values and then applied the dimensionality reduction technique of a low variance filter to reduce the RNA-Seq gene features down to 10,381. Next, they reformatted the RNA-Seq gene features as two-dimensional images and classified the tumors using a _convoluted neural network_ model. The previous studies, however, have limitations. For example, many studies do not utilize dimensionality reduction to reduce and optimize the number of gene features needed for modeling. In fact, to the best of our knowledge, no previous technique has utilized a factor analysis to reduce the number of features.