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Activity Number: 544
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320192 View Presentation
Title: Bayesian Neural Network for Predicting Survival Time of Competing Risks
Author(s): Taysseer Sharaf*
Companies: Slippery Rock University
Keywords: Survival Analysis ; Competing Risks ; Neural Networks ; Bayesian Learning ; Hybrid Monte Carlo

A new method of using artificial neural network to predict the survival probability function of competing risks will be presented. This new method of utilizing artificial neural network was trained using an updated version of the Bayesian Learning method that was introduced by Neal in 1996 and uses the hybrid Monte Carlo algorithm. A comparison was also structured to test the effectiveness of the updated Bayesian learning to the existing one. All of the developed models were applied to real data, taken from the Surveillance, Epidemiology, and End Results Program (SEER), which is conducted by the National Cancer Institute.

Authors who are presenting talks have a * after their name.

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