Review ArticleOpen Access

Restricted Boltzmann Machine and its Potential to Better Predict Cancer Survival

Volume 6 - Issue 1

**Ruibang Luo*, Wen Ma and Tak-Wah Lam**

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- Department of Computer Science, The University of Hong Kong, China
*Corresponding author: Ruibang Luo, Assistant Professor, Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China

Received: June 20, 2018; Published: June 26, 2018

DOI: 10.26717/BJSTR.2018.06.001305

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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.

Abbrevations: RBM: Restricted Boltzmann Machine, GBM: Glioblastoma, AFP: Alpha-Fetoprotein, AUC: Area Under The Curve

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