The pathogenesis of breast cancer is not the same in all countries and regions; therefore, some existing breast cancer risk assessment models are not well adapted to all countries and regions, including China. This chapter proposes a new model called BCRAM (a social-network-inspired breast cancer risk assessment model) that depends on epidemiological factors and is more adaptive to a populous country like China than are models based on genetics. The proposed model utilizes the similarities among epidemiological factors to construct a breast cancer high-risk group, the members of which have high similarity with breast cancer patients. Then, three tests based on real data are used to determine the assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. The F-measure value is 0.696, which is the largest among all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high percentage of patients ultimately discovered to have breast cancer in the future. Therefore, the model is significant for the prevention and control of breast cancer. And the unique design of the method for selecting risk factors related to breast cancer results in our model having good generality overall, and it can be generalized to other countries and regions.
|Title of host publication||Cyber-Enabled Intelligence|
|Editors||Huansheng Ning, Liming Chen, Ata Ullah, Xiong Luo|
|Place of Publication||New York|
|Number of pages||21|
|Publication status||Published - 8 Aug 2019|
Triboan, D., Chen, L., Chen, F., & Wang, Z. (2019). Multi-granular Activity Recognition within a Multiple Occupancy Environment: Cyber-Enabled Intelligence. In H. Ning, L. Chen, A. Ullah, & X. Luo (Eds.), Cyber-Enabled Intelligence (1st ed., pp. 149-169). https://doi.org/10.1201/9780429196621-8, https://doi.org/10.1201/9780429196621