Modeling and Predicting the Survival of Breast Cancer Patients via Deep Neural Networks and Bayesian Algorithm

Document Type : Original Paper


1 Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

2 Department of Biomedical Engineering, Meybod University, Meybod, Iran


Purpose: Due to the importance and prevalence of breast cancer as the second leading cause of death among cancer patients worldwide, access to models that can accurately predict the survival of breast cancer patients is very important. This study aimed to use an optimized deep neural network to predict the survival of breast cancer patients.

Material and Methods: The present study is an analytical study. The information utilized in this research is derived from the METABRIC database, associated with the molecular categorization of breast cancer patients, from the International Federation of Breast Cancer's Molecular Taxonomy Data. The total number of patients studied is 1,981. Of these, 888 patients were under care until their death, and the remaining patients withdrew from the study during its course. In this database, 22 clinical features of patients have been considered, which includes a total of 6 quantitative features and 15 qualitative features. A deep neural network model called the optimized DeepHit is used to predict survival. The optimal parameters for specific variables of the neural network are obtained by the Bayesian algorithm. Results: The optimized model has achieved the criterion of c_index = 0.748, which is a criterion for measuring the capability of survival analysis models.

Conclusions: The performance of the proposed model was compared with previous models based on real and synthetic datasets. The simulation results show that the optimized DeepHit has achieved great performance and statistically significant improvements over previous advanced methods.


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 13 May 2023
  • Receive Date: 29 November 2022
  • Revise Date: 10 May 2023
  • Accept Date: 13 May 2023