ARTIFICIAL NEURAL NETWORKS IN THE RECOGNITION OF PATIENTS AT HIGH RISK OF CARDIOVASCULAR DISEASE

We have previously shown, in a large cross-sectional study, that intima media thickness (IMT) of carotid arteries, as measured in the normal clinical practice, correlated with most of the vascular risk factors (VRFs), and discriminated well between patients with and without previous history of cardiovascular events. This approach, however, does not provide information on the cardiovascular risk of individual patients, but rather it allows the identification of groups of patients. Artificial neural networks (ANNs) are computer algorithms inspired by highly interactive processing of human brain. ANNs are able to recognize patterns and, when exposed to complex data sets, they have the capability to learn the underlying mechanisms relating different variables and to recognize classification tasks. In present study, we have assessed the performance of ANNs in the recognition of patients at low (n= 464) or high risk (n=114) of vascular disease on the basis of conventional VRFs, IMT or both. Patients were arbitrarily assigned to the high risk group when suffering from overt cardiovascular disease. With optimal settings, a prediction accuracy of about 87% were obtained when conventional VRFs were used as input variables in the ANN classification system, whereas a 77% was obtained with only ultrasonic variables. The addition to ultrasonic variables of gender, age, weight, height and body mass index led this figure to 86%. When ANNs were allowed to choose automatically the relevant input data (I.S. system) 31 variables were selected comprising 6 ultrasonic variables. With this input, the performance of ANNs in the classification task increased reaching a prediction accuracy about 92%, with 100% of correct classification of high risk patients. In conclusion, ANN technology is promising in the development of highly specific diagnostic tools to be used for patients’ classification into low or high risk classes.