# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 3 Results
### Results  
#### 3.3.1 Classification Performance of Different Machine Learning Methods for Five Cancer Subtypes  
We first compared the MLW-gcForest algorithm with the SVM, KNN, LR, and standard gcForest algorithms for five cancer subtypes to demonstrate the superiority of our proposed algorithm. Figure 5 shows the performances of the different machine learning methods on the five cancer subtypes. Figure 5 shows the classification results for breast cancer, which are divided into 4 subtypes: (luminal A (231), basal-like (98), luminal B (127), and HER2-enriched (58)). The experimental results suggest that MLW-gcForest obtains the highest AUC (0.99), which is 0.01 higher than that of standard gcForest and is always superior to the other traditional machine learning methods. Furthermore, the MLW-gcForest algorithm obtains an ACC of 90.5%, Pre of 90.8%, Recall of 89.6%, and F\({}_{1}\) of 90.4%, which are superior to the results for the gcForest algorithm and traditional machine learning algorithms. Figure 5 shows the classification results for LUAD, which are divided into three subtypes: (bronchioid (120), magnoid (83), and squamoid (114)). MLW-gcForest obtains the highest AUC (0.92), slightly higher than that of standard gcForest and consistently better than those of the other conventional methods. Furthermore, MLW-gcForest is slightly better than gcForest and significantly outperforms the conventional machine learning methods for the indicators ACC, Pre, Recall, and F\({}_{1}\). Figure 5 shows the classification results for LIHC, which are divided into four subtypes (Hepatitis B Virus (HBV) (73), Hepatitis C Virus(HCV) (15), hepatitis C antibody (56), and hepatitis B surface antigen (23)). MLW-gcForest obtains the highest AUC (0.91) and has better classification performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cancer** & **Total Samples** & **Available Samples** & **Feature Dimensions** & **Subtype Classes** \\ \hline Breast invasive carcinoma & & & & \\ (BRCA) & 1247 & 514 & 350 & 4 \\ Glioblastoma (GBM) & 629 & 546 & 240 & 4 \\ Lung adenocarcinoma & 706 & 317 & 380 & 3 \\ (LUAD) & & & & \\ Stomach adenocarcinoma & 580 & 508 & 250 & 4 \\ (STAD) & & & & \\ Liver hepatocellular & 167 & 167 & 390 & 4 \\ carcinoma (LIHC) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information for five cancer datasets. than the gcForest algorithm; both algorithms perform significantly better than the traditional SVM, KNN, LR, and RF algorithms. The classification performance of MLW-gcForest for LIHC is slightly lower than those for BRCA and LUAD. The reason for this difference may be that the LIHC data set is relatively small, making it difficult to train a model with higher precision. However, in this case, our proposed algorithm still achieves better classification performance than that of the other algorithms, further proving the effectiveness of our algorithm on small data sets. Figure 5d shows the classification results for GBM, which are divided into four subtypes (classical (150), mesenchymal (166), neural (90), and proneural (140)). MLW-gcForest obtains the same AUC as standard gcForest, which is better than those of the other conventional machine learning methods. Besides, the MLW-gcForest algorithm obtains an ACC of 0.885, a Pre of 0.863, a Recall of 0.878, and an F\({}_{1}\) of 0.870, outperforming the gcForest algorithm (ACC of 0.836, Pre of 0.857, Recall of 0.850, and F\({}_{1}\) of 0.853). Both algorithms perform significantly better than the traditional machine learning methods regarding ACC, Pre, Recall, F\({}_{1}\), and AUC. Figure 5e shows the classification results for STAD, which are divided into four subtypes (stomach, adenocarcinoma, not otherwise specified (NOS) (208), stomach, intestinal adenocarcinoma (207), stomach, adenocarcinoma, diffuse type (80), stomach adenocarcinoma, signet ring type (13)). MLW-gcForest obtains the same AUC as standard gcForest and obtains higher ACC, Pre, Recall, and F\({}_{1}\) than gcForest.