Acute myocardial infarction (AMI) is complex disease; its pathogenesis is not completely understood and several variables are involved in the disease.. The aim of this paper was to assess: 1) the predictive capacity of Artificial Neural Networks (ANNs) in consistently distinguishing the two different conditions (AMI or control). 2) the identification of those variables with the maximal relevance for AMI. Genetic variances in inflammatory genes and clinical and classical risk factors in 149 AMI patients and 72 controls were investigated. From the data base of this case/control study 36 variables were selected. TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 18 variables. Fitness, sensitivity, specificity, overall accuracy of the association of these variables with AMI risk were investigated. Our findings showed that ANNs are useful in distinguishing risk factors selectively associated with the disease. Finally, the new variable cluster, including classical and genetic risk factors, generated a new risk chart able to discriminate AMI from controls with an accuracy of 90%. This approach may be used to assess individual AMI risk in unaffected subjects with increased risk of the disease such as first relative with positive parental history of AMI.
A New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm
FERRARI, Roberto;CAMPO, Gianluca Calogero;
2015
Abstract
Acute myocardial infarction (AMI) is complex disease; its pathogenesis is not completely understood and several variables are involved in the disease.. The aim of this paper was to assess: 1) the predictive capacity of Artificial Neural Networks (ANNs) in consistently distinguishing the two different conditions (AMI or control). 2) the identification of those variables with the maximal relevance for AMI. Genetic variances in inflammatory genes and clinical and classical risk factors in 149 AMI patients and 72 controls were investigated. From the data base of this case/control study 36 variables were selected. TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 18 variables. Fitness, sensitivity, specificity, overall accuracy of the association of these variables with AMI risk were investigated. Our findings showed that ANNs are useful in distinguishing risk factors selectively associated with the disease. Finally, the new variable cluster, including classical and genetic risk factors, generated a new risk chart able to discriminate AMI from controls with an accuracy of 90%. This approach may be used to assess individual AMI risk in unaffected subjects with increased risk of the disease such as first relative with positive parental history of AMI.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
