## 5 Related Work Comparisons  
Accuracy and performance comparisons from related studies are shown in Table 4. Our study and all comparative studies obtained their cancer RNA-seq data from the same source, _The Cancer Genome Atlas (TCGA)_ project. What differs is what subset of the available cancer RNA-seq data other studies elected to use. The Tumor Classifier Project (TC1) [26] and [PERSON] et al. 's [13] study used the same dataset as ours. The TC1 project did not filter out genes with zero expression across all samples, nor did it normalize gene expression beyond the TPM scale during data preprocessing. Additionally, dimensionality reduction was not utilized. TC1 used a _one-dimensional convolutional neural network_ model to classify the entire 5400 sample \(\times\) 60,483 RNA-Seq feature dataset, with an overall accuracy of 98%. Their result was used as the initial accuracy benchmark for our study. [PERSON] et al. [13] achieved their best accuracy (99%) with a _Light Gradient Boosting Machine_ model. The _Support Vector Classification_ model in [13] (similar to our top model) only yielded an accuracy of 96% compared to our 99%. Figure 8: Model accuracy chart. Table 4 also shows that, in [14], the authors achieved their best accuracy (89%) with a _logistic regression_ model. The highest accuracy for [15] was 97%. Both [16] and [17] achieved an overall classification accuracy of 92%. Table 5 contains comparisons of individual tumor classification accuracies from related studies. The cancers with accuracies below 98% are highlighted in red font. Across all the studies, bladder (BLCA), cervical (CESC), both types of kidney (KIRP, KIRP), and lung squamous cell (LUSC) cancers were the most challenging to accurately classify. Overall, our _Support Vector Classification_ model applied to the 68 factors dataset from the random forest top features outperformed other similar studies across the majority of individual tumors. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Related Work** & **Best Model and Dataset Type** & **Accuracy** \\ \hline Tumor Classifier & 1D Convoluted Neural Network Model & \\ Project (TC1) & – 5400 Samples \(\times\) 60,484 RNA-Seq Features & 98\% \\
2019 [26] & – 18 Target Tumor Types & \\ \hline [PERSON] & Light Gradient Boosting Machine Model & \\
2022 [13] & – 1500 Samples \(\times\) 60,484 RNA-Seq Features & 99\% 1 \\  & – 5 Target Tumor Types & \\ \hline [PERSON] et al. & Logistic Regression Model & \\
2020 [14] & – 4731 Samples \(\times\) 19,737 RNA-Seq Features & 89\% 2 \\  & – 5 Target Breast Tumor Subtypes & \\ \hline [PERSON] et al. & MI\_DenseNetCAM Model (Deep Learning) 4 & \\
2021 [15] & – 10,267 Samples \(\times\) 20,531 RNA-Seq Features & \\  & – Reduced to 10,267 \(\times\) 3600 RNA–Seq Features & 97\% \\  & – 33 Target Tumor Types & \\ \hline [PERSON] et al. & k–Nearest Neighbors Model & \\
2017 [16] & – 9096 Samples \(\times\) 20,000 RNA-Seq Features & 92\% 3 \\  & – 31 Target Tumor Types & \\ \hline [PERSON] et al. & 2D Convoluted Neural Network Model & \\
2018 [17] & – 10,267 Samples \(\times\) 20,531 RNA-Seq Features & 92\% 3 \\  & – 33 Target Tumor Types & \\ \hline Our Top Model & Support Vector Machine & \\
2023 & – 5400 Samples \(\times\) 60,484 RNA-Seq Features & \\  & – Reduced to 5400 Samples \(\times\) 68 Factor Features & \\  & – 18 Target Tumor Types & \\ \hline \hline \end{tabular} \({}^{1}\) Synthetic Minority Oversampling Technique (SMOTE) applied to correct imbalanced classes in the dataset (original sample size = 801). 2 The breast cancer classification accuracy in our top model was 100%. 3 Comparable accuracy on the 18 tumors from our study = 97%. 4 Transformed RNA–Seq gene expressions into a 2D image for modeling.  
\end{table} 
Table 4: Overall accuracy comparisons.