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Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination

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10 Author(s)
Lisboa, P.J.G. ; Sch. of Comput. & Math. Sci., Liverpool John Moores Univ., Liverpool, UK ; Etchells, T.A. ; Jarman, I.H. ; Arsene, C.T.C.
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Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995).

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Neural Networks, IEEE Transactions on  (Volume:20 ,  Issue: 9 )