Bayesian Neural Network Applied in Medical Survival Analysis of Primary Biliary Cirrhosis

Abstract

A benchmark medical study is realized for a Primary Biliary Cirrhosis (PBC) dataset by using two different versions of a Bayesian Neural Network (BNN) entitled Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD). The two BNN versions are based on two different compensation mechanisms which are designed to preserve the numerical stability of the PLANN-CR-ARD model and to calculate the marginalized network results. The predictions of the PLANN-CR-ARD models are comparable to the non-parametric estimates obtained through the survival analysis of the PBC dataset. The input variables from the PBC dataset which can have a strong influence on the outcome of the disease are determined. The PLANN-CR-ARD models can be used to investigate the non-linear inter-dependencies between the predicted outputs and the input data which consist of the characteristics of the PBC patients.