Restricted Boltzmann Machine and its Potential to Better Predict Cancer Survival

Abstract

Traditional methods to predict cancer survival include Competing-Risk Regression and Cox Proportional Hazards Regression; both require the hazard of input variables to be proportionate, limiting the use of non-proportionate measurements on miRNA inhibitors and inflammatory cytokines. They also require imputation at missing data before prediction, adding fallible workloads to the clinical practitioners. To get around the two requirements, we applied Restricted Boltzmann Machine (RBM) to two patient datasets including the NCCTG lung cancer dataset (228 patients, 7 clinicopathological variables) and the TCGA Glioblastoma (GBM) miRNA sequencing dataset (211 patients, 533 mRNA measurements) to predict the 5-year survival. RBM has achieved a c-statistic of 0.989 and 0.826 on the two datasets, outperforming Cox Proportional Hazards Regression that achieved 0.900 and 0.613, respectively