Title :
PLANN-CR-ARD model predictions and Non-parametric estimates with Confidence Intervals
Author :
Arsene, Corneliu T C ; Lisboa, Paulo J.
fDate :
July 31 2011-Aug. 5 2011
Abstract :
This paper investigates the performance of the PLANN-CR-ARD network predictions through a comparison with the confidence intervals and the non-parametric estimates obtained from the survival analysis of a Primary Billiary Cirrhosis (PBC) dataset. The predictions of the PLANN-CR-ARD model are marginalized using two methods: approximation of the integral of marginalization and the Monte Carlo method. The numerical results show that the PLANN-CR-ARD predicts very well, the results being situated within the confidence intervals of the non-parametric estimates. The Model Selection is also performed on the same dataset. The PLANN-CR-ARD can be used to explore the non-linear interdependencies between the predicted outputs and the input data which in survival analysis describes the characteristics of the patients.
Keywords :
Monte Carlo methods; estimation theory; neural nets; patient treatment; Monte Carlo method; PLANN-CR-ARD model predictions; automatic relevance determination; competing risks; confidence intervals; model selection; nonparametric estimates; partial logistic artificial neural network; patient survival analysis; primary billiary cirrhosis dataset; Diseases; Hazards; Indexes; Liver; Mathematical model; Numerical models; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033409
