# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 6 Analysis of Results
Overall, the data of the models are relatively smooth, indicating that the models have achieved their best results while having stability, and the test results are representative. Shown in Table 5 is the model training results of the fusion model designed in this paper under all sample data. It is clear from the data in the table that the accuracy of the three individual models, random forest, SVM and neural network, achieves 95%, while the recall and accuracy also remain between 95% and 96% under all the sample data. The training results of the fusion model, on the other hand, are not much different from each of the other individual models and are slightly better than the other individual models, reaching 98.49% in terms of accuracy and maintaining 98.24% in terms of recall, as well as accuracy. The BN and C3R models designed by the two articles cited in this paper achieved 98.24% and 98.23% accuracy, respectively, in this case, and the model prediction results are significantly better than the separate models in this paper. Yet, the overall performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **f1-Score** & **Precision** \\ \hline random forest & 93.85\% & 93.85\% & 93.88\% & 94.04\% \\ \hline SVM & 93.85\% & 93.83\% & 93.84\% & 93.85\% \\ \hline neural network & 96.49\% & 96.49\% & 96.50\% & 96.57\% \\ \hline dual-fusion model & 98.24\% & 98.24\% & 98.23\% & 98.23\% \\ \hline triple-fusion model & 98.25\% & 98.24\% & 98.26\% & 98.24\% \\ \hline BN & 98.24\% & 98.23\% & 98.25\% & 98.24\% \\ \hline C3R & 98.23\% & 98.23\% & 98.26\% & 98.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training results for each model taking 25 extracted features under 30% small sample data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **f1-Score** & **Precision** \\ \hline random forest & 95.90\% & 94.02\% & 91.23\% & 92.23\% \\ \hline SVM & 94.35\% & 90.35\% & 90.35\% & 91.56\% \\ \hline neural network & 81.37\% & 80.76\% & 81.15\% & 82.11\% \\ \hline dual-fusion model & 98.23\% & 98.22\% & 98.23\% & 98.25\% \\ \hline triple-fusion model & 98.24\% & 98.23\% & 98.24\% & 98.26\% \\ \hline BN & 97.23\% & 97.21\% & 97.23\% & 97.26\% \\ \hline C3R & 97.24\% & 97.22\% & 97.21\% & 97.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training results for each model taking five extracted features under 30% small sample data. effect was still different from the fusion model designed in this paper. At the same time, the model data fluctuates only slightly, indicating that each model has achieved the best and most stable test effect, and the test results are representative. Based on the data in the Table 6, we hereby conclude that the fusion model designed and implemented in this paper tests almost identically in both environments of 30% small sample data and full sample data, and the accuracy is maintained at 98.22% in both cases, while the recall of the fusion model reaches 98.21% and the f1_score reaches 98.21%, compared to the fusion model with only a slight bias in the 30% small sample setting. On the contrary, although the training results of the three individual models of random forest, SVM, and neural network are not much different from the fusion model under the full sample data, the accuracy rate of the three individual models can successfully reach 96.21%. However, the accuracy rate of the model training decays relatively severely under the premise of the small sample data, while the accuracy rate of the neural network model under the small sample environment of 30% can only reach 80%, and the recall rate can also only reach 80.76%, which is a significant decay compared to the 96.21% accuracy of the neural network model in the full sample case. The models designed by the two articles cited in this paper can also maintain some excellent prediction results under the premise of small samples and outperform other individual models, but the results are much worse than the fusion models designed in this paper.