Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model

In view of the characteristics of long course and mild disease development of breast cancer, a multi-task recurrent neural network banded regression model is proposed to analyze individual survival of breast cancer. First, a multi-task banded regression model based on recurrent neural network is proposed to optimize the individual survival analysis of patients by identifying the difference in the effects of various pathological features on different patients. Then, the form of the banded check matrix is expanded and its effect on the hazard distribution of patient is investigated. Finally, the survival analysis on the real datasets of breast cancer shows obvious differences among different patients, which verifies the validity of the model. The survival analysis on two real datasets of breast cancer shows that the C-index of the multi-task banded regression model based on recurrent neural network is greatly improved compared with the Cox regression model commonly used in medicine, and has a smaller 95% confidence interval.