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 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 …

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 …

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 …

We constructed and internally validated an artificial neural network (ANN) model for
prediction of in-hospital major adverse outcomes (defined as death, cardiac arrest, coma …

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 …

Background: As the era of big data analytics unfolds, machine learning (ML) might be a
promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive …

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 …

Risk‐stratification models based on pre‐operative patient and disease characteristics are
useful for providing individual patients with an insight into the potential risk of complications …

The traditional approach to developing models predictive of cardiac events has been to
perform logistic regression (LR) analysis on a variety of potential predictors. An alternative is …