# A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
## 1 Introduction  
Since the middle and end of the 20 th century, the global incidence of breast cancer has been rising year by year. According to cancer statistics from the World Health Organization, breast cancer is currently the most life-threatening disease for women, with a prevalence rate of 12.5% in some developed countries. In addition, studies have shown that it is the most diagnosed type of cancer in the world [1]. Although the breast cancer prevalence rate remains low in China, the overall situation is still not optimistic, since the incidence rate and mortality rate of breast cancer among women in China are both higher than the world standard, and worse still, the incidence of breast cancer is increasingly prevalent among the younger population. GLOBOCAN is a significant project of the International Agency for Research on Cancer (IARC), which provides data on incidence, mortality, and cancer trends for 36 types of cancer in 185 countries/regions worldwide. It estimates that the most prevalent cancer among Chinese women is cancer in the breast. As early as eight years ago, in 2009, the Bureau of Disease Control and Prevention of the Ministry of Health of China, in collaboration with the Cancer Center, conducted a detailed data analysis of breast cancer data, suggesting that in regions within China where data have been documented, the incidence rate of breast cancer has jumped to number one, makingit the most serious malignancy impeding women's health. Breast cancer has become the fastest growing cancer in the last decade, with a 37.8% jump in terms of incidence rate over the last decade. Therefore, breast cancer is no longer a health problem for individual patients but rather has invariably become a major public health problem. Traditionally, a breast cancer tumor is diagnosed by observing the presence of the tumor, the degree of cancer, metastasis location, etc. in a tissue biopsy and pathology section. However, the amount of such data is basically in the order of 10 billion pixels. Therefore, if the number of patients exceeds capacity, the workload of medical staff could become extremely high. In addition, the entire diagnosis process of breast cancer is not only limited by medical levels in different regions but is also easily interfered by the personal, subjective factors of doctors. As science and technology advances, medical data are no longer simply patient information, examination records, etc. Additionally, the data format has also changed radically and morphed into a mixture of video and voice, or a mixture of images and text. Such a complex data model has also become unique to healthcare data. On the one hand, complex data increase the possibility of finding potential causes; on the other hand, however, they increase the cost of medical detection and raise the complexity of algorithms, which in turn makes it difficult to reach the right balance in a limited sample. Under this premise, this paper designs feature selection methods to find the important parts of complex data to construct a minimal medical aid diagnosis model. Furthermore, a well-designed deep learning model is introduced to further tune the minimal medical diagnosis model under the condition of sufficient resources. From the analysis of the experimental results, under limited conditions, the algorithm designed in this paper achieves the optimal effect, the value of the model prediction result (f1_score) reaches 98.24%, the value of recall reaches 98.23%, and the accuracy rate reaches 98.23%. Overall, the overall effect of the model is better than other individual algorithmic models, indicating that in the new development period, it is crucial to apply machine learning techniques in the medical-data-mining segment. The effective use of medical data requires fast and efficient mathematical methods. The current machine learning and artificial intelligence technologies have advanced dramatically [2].