Recognition of patients with cardiovascular disease by artificial neural networks

Damiano Baldassarre, Enzo Grossi, Massimo Buscema, Marco Intraligi, Mauro Amato, Elena Tremoli, Linda Pustina, Samuela Castelnuovo, Silvia Sanvito, Lorenzo Gerosa, Cesare R. Sirtori

Research output: Contribution to journalArticlepeer-review

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. AIMS. 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. 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 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.

Original languageEnglish
Pages (from-to)630-640
Number of pages11
JournalAnnals of Medicine
Volume36
Issue number8
DOIs
Publication statusPublished - 2004

Keywords

  • Artificial intelligence
  • Diagnosis
  • Intima media thickness
  • Neural networks

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of 'Recognition of patients with cardiovascular disease by artificial neural networks'. Together they form a unique fingerprint.

Cite this