title: Recognition of patients with cardiovascular disease by artificial neural networks.
abstract: 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.
  
  
  OBJECTIVE
  A database, of 949 patients and 54 variables, was analysed to evaluate the capacity of ANNs to recognise patients with (VE+, n = 196) or without (VE-, n = 753) a history of vascular events on the basis of vascular risk factors (VRFs), carotid ultrasound variables (UVs) or both.
  
  
  METHODS
  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 of the ANN providing the best results were 80.8% and 79.2%, respectively. The addition of gender, age, weight, height and body mass index to UVs increased accuracy of prediction to 83.0%. When the ANNs were allowed to choose the relevant input data automatically (I.S. system-Semeion), 37 variables were selected among 54, five of which were UVs. Using this set of variables as input data, the performance of the ANNs in the classification task reached a prediction accuracy of 85.0%. with the 92.0% correct classification of VE+ patients.
  
  
  CONCLUSIONS
  Artificial neural network technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of cardiovascular diseases.
authors.affiliation: E Grossi Paoletti Center, Department of Pharmacological Sciences and Niguarda Hospital, University of Milan, Milan, Italy.
authors.forename: Damiano
authors.initials: D
authors.lastname: Baldassarre
authors.forename: Enzo
authors.initials: E
authors.lastname: Grossi
authors.forename: Massimo
authors.initials: M
authors.lastname: Buscema
authors.forename: Marco
authors.initials: M
authors.lastname: Intraligi
authors.forename: Mauro
authors.initials: M
authors.lastname: Amato
authors.forename: Elena
authors.initials: E
authors.lastname: Tremoli
authors.forename: Linda
authors.initials: L
authors.lastname: Pustina
authors.forename: Samuela
authors.initials: S
authors.lastname: Castelnuovo
authors.forename: Silvia
authors.initials: S
authors.lastname: Sanvito
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Gerosa
authors.forename: Cesare R
authors.initials: CR
authors.lastname: Sirtori
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000000981:diagnostic imaging; D017536:Carotid Artery, Common / Q000000981:diagnostic imaging; D002343:Carotid Artery, Internal / Q000000981:diagnostic imaging; D003327:Coronary Disease / Q000175:diagnosis; D003430:Cross-Sectional Studies; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014463:Ultrasonography