Predicting the Survival of Breast Cancer Patients via Deep Neural Networks

Predicting and estimating the time it takes for an event of interest to occur base on available information is special assistance in how to deal with the event and handle it or provide solution to prevent the occurrence of the event. In medicine, valuable information about evaluating the types of treatments and prognosis and providing solution to handle event can be gained by predicting the time that an event occurrence according to information recorded from patients. Many statistical solutions have been proposed for predicting the time that an event occurrence and the most professional method is Survival Analysis. The purpose of Survival Analysis is to predict the time that an event occurrence a model effective parameters in estimating the time, which can be control or eliminating problematic factors. Due to the importance and prevalence of breast cancer as the second leading cause of death among cancer patients in the world, access to models that can accurately predict the survival of breast cancer patients is very important. The present study is an analytical study. The data used in this study are taken from The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database, which is related to which is related to the molecular classification of breast cancer patients. The total number of patients studied was 1981. Of these, 888 patients were in care until the time of death and the rest did not continue the study during the study. In this database, 21 clinical features of patients have been considered, which includes a total of 6 quantitative features and 15 qualitative features. To predict survival, a deep neural network model called the optimized DeepHit is used. The optimized model has achieved the criterion of c_index = 0.73, which is a criterion for measuring the capability of survival analysis models. Comparisons with previous models based on real and synthetic datasets show that the optimized DeepHit has achieved great performance and statistically significant improvements over previous advanced methods.