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 #clinicopathologicalvariables) 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.
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