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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

ISBN: 978-989-758-068-0

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, 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. (More)

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Paper citation in several formats:
Licastro, F.; Ianni, M.; Ferrari, R.; Campo, G.; Buscema, M.; Grossi, E. and Porcellini, E. (2015). A New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 252-259. DOI: 10.5220/0005183102520259

@conference{healthinf15,
author={Federico Licastro. and Manuela Ianni. and Roberto Ferrari. and Gianluca Campo. and Massimo Buscema. and Enzo Grossi. and Elisa Porcellini.},
title={A New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005183102520259},
isbn={978-989-758-068-0},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - A New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm
SN - 978-989-758-068-0
AU - Licastro, F.
AU - Ianni, M.
AU - Ferrari, R.
AU - Campo, G.
AU - Buscema, M.
AU - Grossi, E.
AU - Porcellini, E.
PY - 2015
SP - 252
EP - 259
DO - 10.5220/0005183102520259

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