# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 6 Analysis of Results  
When using the Wisconsin breast cancer dataset as the dataset for this experiment, if the entire data in the dataset are used for model testing, then the accuracy rate can reach close to 100%, whether via random forest or SVM. However, if a small sample of 30% of the data in the dataset are randomly selected for model testing, then the accuracy of the model test drops significantly to 80% using either random forest or SVM. In the case of taking a small 30% of sample data, the data performance of the fusion model designed and implemented in this paper is the same as the performance using 100% data; the better the model accuracy is than the random forest, the better the accuracy of the model when using the random forest and SVM model alone. In the paper \"Bayesian network models with decision tree analysis for management of childhood malaria in Malawi\" [14], a similar data classification analysis method was used to analyze the related diseases. It used the BN (Bayesian network) model to predict the attributes associated with malaria. The manually created BN model in this article performs significantly better in terms of prediction accuracy but performs slightly worse in terms of f1_score, as well as of recall, compared to the fusion model designed for this study. In a similar comparison, in the article \"In comparison, a data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea\" [15], which uses the C3R (colorectal cancer chemotherapy recommender) chemotherapy recommendation model to perform predictive processing of relevant data involved in clinical care, since this model is optimized based on the existing CDSS. It outperforms some of the other models in terms of model performance, but it still lacks in terms of accuracy and recall when compared with the fusion model designed and implemented in this paper. Shown in Table 3 is data such as accuracy, as well as precision, for training with a 30% data sample using the three models of this paper alone and using the five extracted features for model training. The data in the table shows that the model accuracy of the fusion model and the random forest and SVM reaches 95%, while the neural network model test accuracy is only 80%, which indicates that the neural network is less effective in model training under the condition of small samples, and the model test training has a higher degree of decay and is less stable compared to other models. The models designed in the two articles cited in this paper were able to outperform other single models in this condition in all data; for example, the accuracy BN reached 98.23% and C3R reached 98.24%. In other words, the model prediction effect has been very good. Figure 10: Heat map of the effect of SVM parameters. However, compared to the overall performance situation of the fusion model it is still slightly inadequate. In general, the data of each model is relatively smooth, indicating that the model has achieved its best results while having stability, and the test results are representative. Shown in Table 4 is its data, such as accuracy, as well as precision for training using 30% of the data sample with the three models of this paper alone. The models are trained using all 25 features involved in this paper. The data in this table shows that the accuracy and accuracy of the models using all 25 extracted features is reduced compared to the above table using 5 extracted features and the accuracy of the model training for both random forest and SVM is maintained at 91%. The neural network likewise barely performs model training properly using all 25 features, with accuracy remaining at 80%. Under these conditions, the model designed by the article cited in this paper also decreases in effectiveness but still outperforms the other individual models overall. Accuracy rates of 98.24% and 98.23% are achieved for both BN and C3R, respectively, which are slightly worse than the fusion model prediction results.