Authors:
Federico Licastro
1
;
Manuela Ianni
1
;
Roberto Ferrari
2
;
Gianluca Campo
2
;
Massimo Buscema
3
;
Enzo Grossi
4
and
Elisa Porcellini
1
Affiliations:
1
University of Bologna, Italy
;
2
University of Ferrara, Italy
;
3
Semeion and Research Centre of Sciences of Communication, Italy
;
4
Bracco Foudation and Milan, Italy
Keyword(s):
Acute Myocardial Infarction (AMI), Artificial Neural Network (ANN), Twist Algorithm, Risk Chart.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cognitive Informatics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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, th
e 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.
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