Recognition of patients with cardiovascular disease by artificial neural networks.

BACKGROUND. Artificial neural networks (ANNs) are computer algorithms inspired by the highly interactive processing of the human brain. When exposed to complex data sets, ANNs can learn the mechanisms that correlate different variables and perform complex classification tasks. AIMS. A database, of 949 patients and 54 variables, was analysed to evaluate the capacity of ANNs to recognise patients with ( VE +  = 196) or without (VE −  = 753) a history of vascular events on the basis of vascular risk factors (VRFs), carotid ultrasound variables (UVs) or both. METHOD. The performance of ANN was assessed by calculating the percentage of correct identifications of VE + and VE − patients (sensitivity and specificity, respectively) and the prediction accuracy (weighted mean between sensitivity and specificity).  RESULTS. The results showed that ANNs can be trained to identify VE + and VE − subjects more accurately than discriminant analyses. When VRFs and UVs were used as input variables, the prediction accuracies...