# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 3 Introduction to the Experiment  
_Wisconsin Breast Cancer Dataset_  
As shown in Figure 3, the breast cancer patient dataset adopted in this paper is from the University of California, Irvine's machine learning dataset repository, in which the Wisconsin breast cancer dataset was selected.  
The goal of this dataset is to predict whether the subject's breast tumor is benign or malignant, and the dataset is in its original format form, as shown in the following Table 1.  
The dataset includes a total of 569 patient data with 32 attributes per patient. The first of these attributes is the patient's ID number, the second is the diagnosis, and the third through the thirty-second features are computed from digitized images of fine needle  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**1** & **ID** & **Diagnosis** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Concave** \\ \hline
2 & 842302 & **M** & 17.99 & 10.38 & 122.8 & 1001 & 0.1184 & 0.2776 & 0.3001 & 0.1471 \\ \hline
3 & 842517 & **M** & 20.57 & 17.77 & 132.9 & 1325 & 0.08474 & 0.07864 & 0.0869 & 0.07017 \\ \hline
4 & 84300903 & **M** & 19.69 & 21.25 & 130 & 1203 & 0.1096 & 0.1599 & 0.1974 & 0.1279 \\ \hline
5 & 84348301 & **M** & 11.42 & 20.38 & 77.58 & 386.1 & 0.1425 & 0.2839 & 0.2414 & 0.1052 \\ \hline
6 & 84358402 & **M** & 20.29 & 14.34 & 135.1 & 1297 & 0.1003 & 0.1328 & 0.198 & 0.1043 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Breast Cancer Data Raw Format.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Number** \\ \hline diagnosis & M = malignant, B = benign \\ \hline mean radius & radius \\ \hline mean texture & texture \\ \hline mean perimeter & perimeter \\ \hline mean area & area \\ \hline mean smoothness & smoothness \\ \hline mean compactness & compactness \\ \hline mean concavity & concavity \\ \hline mean concave points & concave points \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset feature interpretation.  
Figure 3: The breast cancer patient dataset used in this paper.  
aspiration (FNA) of breast masses. These 10 features cover attributes such as radius, texture, perimeter, and symmetry of the cells, and then the variance, maximum, and mean of their nucleus features are calculated separately. Having a multivariate dataset can provide a lot of useful information for data mining, but it will definitely increase the workload of data collection and data analysis. Factor analysis is a statistical method that uses a few factors to reflect most of the information of the original data. Its main steps include correlation testing of the data, factor extraction, naming and interpretation, calculating factor scores and evaluating the sample. In the process of constructing a model, more variables are not better. If ID number is used as a division attribute, 569 nodes will be generated. However, this is not meaningful at all. Therefore, we remove features like ID number and the predicted results. As a result, there are 30 attributes in the remaining dataset, since not every attribute is well-generalized.