A predictive and prognostic medical study for Primary Billiary Cirrhosis by using a Bayesian Neural Network

A predictive and prognostic benchmark medical study is realized for a Primary Billiary 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. The predictions of the PLANN-CR-ARD models are comparable to the non-parametric estimates obtained through the survival analysis of the PBC dataset. A number of prognostic variables are obtained which can have a strong influence on the outcome of the PBC disease. 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.