A New Method for Predicting Patient Survivorship Using Efficient Bayesian Network Learning

In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis.

Source: Cancer Informatics - February 13, 2014 Category: Cancer & Oncology Authors: Xia JiangDiyang XueAdam BrufskySeema KhanRichard Neapolitan Source Type: research