Effect of Compensation in Partial Logistic Artificial Neural Networks for Medical Survival Analysis

The performance of the non-linear prognostic model `Partial Logistic Artificial Neural Network with Automatic Relevance Determination´ (PLANN-ARD) is observed here and compared to a variant of the model that is trained using a compensating mechanism to account for the skewed distribution in the neural network target vector. The application dataset in this survival analysis is cancer patient information namely diagnosed with Intra-Ocular Melanoma. The outcomes of the two models are compared with the empirical cumulative hazard curve derived from the Kaplan-Meier survival function for a particular population. Three forms of out-come from both the compensated and non compensated models are obtained: the network output itself, marginalised network output and the median of the network output distribution.