# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 3 Results
### Results
#### 3.3.2 Comparison with the State of the Art  
We compared the performance of our proposed algorithm with other studies and used the results reported in these papers as a comparison, as shown in Table 3.  
[PERSON] et al. [22] used a random forest classification algorithm to classify six cancers by extracting five features of the isomiRs and achieved an accuracy of greater than 0.84. The classification accuracy in [PERSON]'s study for BRCA and STAD is lower than that in our study, while the accuracy for LUAD is higher than that of our method. [PERSON] et al. [41] evaluated the ability of isomiRs and used the top 20% abundant isomiRs to construct a binary classifier. The classifier could label tumor samples with 93% average sensitivity. These researchers compare the SVM classification using the miRNA arm (B) expression profile and obtain results as shown in Table 2; the accuracy for BRCA in [PERSON]'s study is similar to that of our method, the accuracy for LIHC is higher than that of our method, and the accuracies for LUAD and STAD are lower than those of our method. Both studies showed that isomiR is very helpful in the classification of cancer subtypes.  
For BRCA subtype classification: [PERSON] et al. [42] obtained an AUC of 0.89, and [PERSON] et al. [43] obtained an ACC of 0.89 and a Pre of 0.90. Our algorithm has a clear advantage in classification performance. The results show that our improved strategy for the gcForest algorithm can learn more discriminative features and achieve better classification performance.  
For LUAD subtype classification: [PERSON] et al. [44] obtained an AUC of 0.92, and [PERSON] et al. [19] obtained an ACC of 0.85 and a Pre of 0.86. [PERSON] et al. [44] obtained the same AUC as that of our proposed algorithm, but these researchers used the highest AUC as the final result, and our result is an average. Therefore, the AUC obtained by our algorithm is more reliable.  
For LIHC subtype classification: [PERSON] et al. [45] obtained an AUC of 0.77 and an ACC of 0.83 and [PERSON] et al. [46] obtained an ACC of 0.87.  
For GBM subtype classification: [PERSON] et al. [47] proposed a three-level machine-learning model and obtained 0.83 AUC and 0.8 ACC. [PERSON] et al. [21] obtained an AUC and ACC of 0.92 and 0.88, respectively.  
Figure 6: The AUC values of different methods for different cancer subtypes with samples of different size scales. (**a**) BRCA, (**b**) LUAD, (**c**) LIHC, (**d**) GBM, (**e**) STAD.  
Though these researchers obtained an AUC higher than that of our method, the ACC value is lower than that of our method.  
We did not find literature for specialized classification of STAD subtypes. The subtype classification of STAD may have not yet found prognostic significance.  
Compared with the methods proposed in the literature, MLW-gcForest achieves better performance for the five cancer subtypes. These results demonstrate that the deep forest structure can capture more complex and diverse features, making this method able to achieve better cancer subtype classification abilities compared with standard gcForest and traditional machine learning algorithms. Furthermore, the proposed multi-level weighting strategy can help deep forests extract more valuable multi-level features, thus effectively improving the classification ability of standard gcForest on small samples of genetic data. Additionally, most of the algorithms in the literature make subtype classifications and prognosis predictions for one particular cancer. Our proposed method can achieve excellent performance in the classification of multiple cancer subtypes, further demonstrating the superiority of our proposed multi-level weighted gcForest algorithm.