This thesis demonstrates the application of a feedforward backpropagation-trained artificial
neural network using the weight-elimination cost function to the estimation of in-hospital …

Objective. To compare the abilities of artificial neural network and logistic regression models
to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery …

The objective was to assess the effectiveness of the weight-elimination cost function in
improving classification performance of artificial neural networks (ANNs) and to observe how …

Objectives The aim of this study was to present an artificial neural network (ANN) model for
the accurate estimation of in-hospital mortality and to demonstrate the validity of the model …

Background. Neural networks are nonparametric, robust, pattern recognition techniques that
can be used to model complex relationships. Methods. The applicability of multilayer …

OBJECTIVE: The artificial neural network model is a nonlinear technology useful for
complex pattern recognition problems. This study aimed to develop a method to select risk …

Previous work on mortality prediction for coronary surgery patients with neural networks has
been hampered by its low rate of occurrence (although this is certainly good from a medical …

Purpose: Neural networks (NNs) have received a great deal of attention over the last few
years. They are being used for prediction and classification areas where regression models …

Objective. To develop a probabilistic neural network (PNN) to estimate mortality risk
following cardiac surgery. Design and setting. The PNN model was created using an …

Preliminary results using an artificial neural network (ANN) on a coronary artery bypass
grafting (CABG) surgery database highlighted challenges when faced with a low …