A New Risk Chart for Acute Myocardial Infarction by a Innovative Algoritm

Federico Licastro, Manuela Ianni, Roberto Ferrari, Gianluca Campo, Massimo Buscema, Enzo Grossi, Elisa Porcellini


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.


  1. Andersson J, Libby P and Hansson GK. 2010 'Adaptive immunity and atherosclerosis' Clin Immunol; vol. 134, no. 1, pp. 33-46.
  2. Biasillo G, Leo M, Della Bona R, Biasucci LM. 2010 'Inflammatory biomarkers and coronary heart disease: from bench to bedside and back' Intern Emerg Med; vol. 5, no. 3, pp. 225-233.
  3. Buri L, Hassan C, Bersani G, Anti M, Bianco MA, Cipolletta L, Di Giulio E, Di Matteo G, Familiari L, Ficano L, Loriga P, Morini S, Pietropaolo V, Zambelli A, Grossi E, Intraligi M, Buscema M, SIED Appropriateness Working Group. 2010 'Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter study' Am J Gastroenterol; vol. 105, no. 6, pp. 1327-1337.
  4. Buscema M, Breda M, Lodwick W. 2013'Training With Input Selection and Testing (TWIST) algorithm: a significant advance in pattern recognition performance of machine learning' Journal of Intelligent Learning Systems and Applications, vol. 5, pp. 29-38.
  5. Buscema M, Grossi E, Capriotti M, Babiloni C, Rossini P. 2010 'The I.F.A.S.T. Model Allows the Prediction of Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment with High Degree of Accuracy' Curr Alzheimer Res; vol. 7, no. 2, pp. 173- 187.
  6. Buscema M. 2004 'Genetic Doping Algorithm (GenD): Theory and Application' Expert Syst; vol. 21, no. 2, pp. 63-79.
  7. Buscema M, Grossi E, Intraligi M, Garbagna N, Andriulli A, Breda M. 2005 'An Optimized Experimental Protocol Based on Neuro-Evolutionary Algorithms. Application to the Classification of Dyspeptic Patients and to the Prediction of the Effectiveness of Their Treatment' Artif Intell Med; vol. 34, no. 33, pp. 279- 305.
  8. Buscema M, Penco S and Grossi E. 2012 'A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory' Neurol Res Int. 478560.
  9. Chiappelli M, Tampieri C, Tumini E, et al. Porcellini E, Caldarera CM, Nanni S, Branzi A, Lio D, Caruso M, Hoffmann E, Caruso C, Licastro F. 2005 'Interleukin6 gene polymorphism is an age-dependent risk factor for myocardial infarction in men' Int J Immunogenet; vol. 32, no. 6, pp. 349-353.
  10. Coppedè F, Grossi E, Migheli F, Migliore L. 2010 'Polymorphisms in Folate-Metabolizing Genes, Chromosome Damage, and Risk of Down Syndrome in Italian Women: Identification of Key Factors Using Artificial Neural Networks' BMC Med Genomics; vol. 3, no. 42.
  11. Coppedè F, Grossi E, Buscema M, Migliore L. 2013 'Application of artificial neural networks to investigate one-carbon metabolism in Alzheimer's disease and healthy matched individuals' PLoS One; vol. 8, no. 8, pp. e74012.
  12. Cross DS, McCarty CA, Hytopoulos E, Beggs M, Nolan N, Harrington DS, Hastie T, Tibshirani R, Tracy RP, Psaty BM, McClelland R, Tsao PS, Quertermous T. 2012 'Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts' Curr Med Res Opin; vol. 28, no. 11, pp. 1819-1830.
  13. Cummings DM, King DE, Mainous AG, Geesey ME. 2006 'Combining serum biomarkers: the association of C-reactive protein, insulin sensitivity, and homocysteine with cardiovascular disease history in the general US population' Eur J Cardiovascular Prev Rehabil; vol. 13, no. 2, pp. 180-185.
  14. Delong ER , Delong DM and Clarke-Pearson DL. 1988 'Comparing the areas under two or more receiver operating characteristic curves: a nonparametric approach' Biometrics; vol. 44, no. 3, pp. 837-45.
  15. Franceschi M, Caffarra P, Savarè R, Cerutti R, Grossi E; Tol Research Group. 2011 'Tower of London test: a comparison between conventional statistic approach and modelling based on artificial neural network in differentiating fronto-temporal-dementia from Alzheimer's disease' Behav Neurol; vol. 24, no. 2, pp. 149-158.
  16. Grimaldi LM, Casadei VM, Ferri C, Veglia F, Licastro F, Annoni G, Biunno I, De Bellis G, Sorbi S, Mariani C, Canal N, Griffin WS, Franceschi M. 2000 'Association of early-onset Alzheimer's disease with an interleukin-1alpha gene polymorphism' Ann Neurol; vol. 47, no. 3, pp. 361-365.
  17. Grossi E, Mancini A and Buscema M. 2007 'International experience on the use of artificial neural networks in gastroenterology' Dig Liver Dis; vol. 39, no. 3, pp. 278-285.
  18. Hamsten A and Eriksson P. 2008 'Identifying the susceptibility genes for coronary artery disease: from hyperbole through doubt to cautious optimism' J Intern Med; vol. 263, no. 5, pp. 538-552.
  19. Ianni M, Callegari S, Rizzo A, Pastori P, Moruzzi P, Corradi D, Porcellini E, Campo G, Ferrari R, Ferrario MM, Bitonte S, Carbone I, Licastro F. 2012 'Proinflammatory genetic profile and familiarity of acute myocardial infarction' Immun Ageing; vol. 9, no. 1, pp. 14.
  20. Juárez-Herrera Ú, Jerjes-Sánchez C, RENASICA II Investigators. 2013 'Risk factors, therapeutic approaches, and in-hospital outcomes in Mexicans with ST-elevation acute myocardial infarction: the RENASICA II multicenter registry' Clin Cardiol; vol. 36, no. 5, pp. 241-248.
  21. Kullo IJ and Ding K. 2007 'Mechanisms of disease: The genetic basis of coronary heart disease' Nat Clin Pract Cardiovasc Med; vol. 4, no. 10, pp. 558-569.
  22. Lahner E, Intraligi M, Buscema M, Centanni M, Vannella L, Grossi E, Annibale B. 2008 'Artificial Neural Networks in the Recognition of the Presence of Thyroid Disease in Patients with Atrophic Body Gastritis' World J Gastroenterol; vol. 14, no. 4, pp. 563-568.
  23. Latorra D, Campbell K, Wolter A, Hurley JM. 2003 'Enhanced allele-specific PCR discrimination in SNP genotyping using 378 locked nucleic acid (LNA) primers' Hum Mutat; vol. 22, no. 1, pp. 79-85.
  24. Levi F, Lucchini F, Negri E, La Vecchia C. 2002 'Trends in mortality from cardiovascular and cerebrovascular diseases in Europe and other areas of the world' Heart; vol. 88, no. 22, pp. 119-124.
  25. Li K, Monni S, Hüsing A, Wendt A, Kneisel J, Groß ML, Kaaks R. 2014 'Primary preventive potential of major lifestyle risk factors for acute myocardial infarction in men: an analysis of the EPIC-Heidelberg cohort' Eur J Epidemiol; vol. 29, no. 1, pp. 27-34.
  26. Licastro F, Chiappelli M, Porcellini E, Campo G, Buscema M, Grossi E, Garoia F, Ferrari R. 2010 'Gene-gene and gene - clinical factors interaction in acute myocardial infarction: a new detailed risk chart' Current Pharmaceutical Des; vol. 16, no. 7, pp. 783- 788.
  27. Licastro F, Chiappelli M, Caldarera CM, Porcellini E, Carbone I, Caruso C, Lio D, Corder EH. 2011 'Sharing pathogenetic mechanisms between acute myocardial infarction and Alzheimer disease as shown by partially overlapping of gene variant profiles' J Alz Dis; vol. 23, no. 3, pp. 421-431.
  28. Lisboa PJC. 2002 'A review of evidence of health benefit from artificial neural networks in medical intervention' Neural Netw; vol. 15, no. 1, pp. 11-39.
  29. Mortensen MB, Sivesgaard K, Jensen HK, Comuth W, Kanstrup H, Gotzsche O, May O, Bertelsen J, Falk E. 2013 'Traditional SCORE-based health check fails to identify individuals who develop acute myocardial infarction' Dan Med J; vol. 60, no. 5, A4629.
  30. Pace F, Riegler G, de Leone A, Pace M, Cestari R, Dominici P, Grossi E, EMERGE Study Group. 2010 'Is It Possible to Clinically Differentiate Erosive from Non erosive Reflux Disease Patients? A Study Using an Artificial Neural Networks-Assisted Algorithm' Eur J Gastroenterol Hepatol; vol. 22, no. 10, pp. 1163-1168.
  31. Packard RR and Libby P. 2008 'Inflammation in atherosclerosis: from vascular biology to biomarker discovery and risk prediction' Clin Chem; vol. 54, no. 1, pp. 24-38.
  32. Pamukcu B, Lip GY, Devitt A, Griffiths H, Shantsila E. 2010 'The role of monocytes in atherosclerotic coronary artery disease' Ann Med; vol. 42, no. 6, pp. 394-403.
  33. Patel RS and Ye S. 2011 'Genetic determinants of coronary heart disease: new discoveries and insights from genome-wide association studies' Heart; vol. 97, no. 18, pp. 1463-1473.
  34. Penco S, Grossi E, Cheng S, Intraligi M, Maurelli G, Patrosso MC, Marocchi A, Buscema M. 2005 'Assessment of the Role of Genetic Polymorphism in Venous Thrombosis Through Artificial Neural Networks' Ann Hum Genet; vol. 69, no. 6; pp. 693- 706.
  35. Penco S, Buscema M, Patrosso MC, Marocchi A, Grossi E. 2008 'New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background' BMC Bioinformatics; vol. 9, pp. 254.
  36. Ravaglia G, Forti P, Maioli F, Orlanducci P, Sacchetti L, Flisi E, Dalmonte E, Martignani A, Cucinotta D, Cavalli G. 2001 'Conselice study: a population based survey of brain aging in a muncipality of the Emilia Romagna region: (A.U.S.L. Ravenna). Design and methods' Arch Gerontol Geriatr Suppl; vol. 7, pp. 313-324.
  37. Ridker PM, Rifai N, Pfeffer M, Sacks F, Lepage S, Braunwald E. 2000 'Elevation of tumor necrosis factor-alpha and increased risk of recurrent coronary events after myocardial infarction' Circulation; vol. 101, no. 18, pp. 2149-2153.
  38. Rotondano G, Cipolletta L, Grossi E, Koch M, Intraligi M, Buscema M, Marmo R; Italian Registry on Upper Gastrointestinal Bleeding (Progetto Nazionale Emorragie Digestive). 2011 'Artificial Neural Networks Accurately Predict Mortality in Patients with Non variceal Upper GI Bleeding' Gastrointest Endoscop; vol. 73, no. 2, pp. 218-226.
  39. Rumelhart DE and McClelland JL. 1982 'An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model' Psychol Rev; vol. 89, no.1, pp. 60-94.
  40. Street ME, Grossi E, Volta C, Faleschini E, Bernasconi S. 2008 'Placental Determinants of Fetal Growth: Identification of Key Factors in the Insulin-Like Growth Factor and Cytokine Systems Using Artificial Neural Networks' BMC Pediatr; vol. 8, no. 24.
  41. Tabaton M, Odetti P, Cammarata S, Borghi R, Monacelli F, Caltagirone C, Bossù P, Buscema M, Grossi E. 2010 'Artificial Neural Networks Identify the Predictive Values of Risk Factors on the Conversion of Amnestic Mild Cognitive Impairment' J Alzheimers Dis; vol. 19, no. 3, pp. 1035-1040.
  42. Yusuf S, Reddy S, Ounpuu S, Anand S. 2001 'Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization' Circulation; vol. 104, no. 22, pp. 2746-2753.
  43. Zhang C. 2008 'The role of inflammatory cytokines in endothelial dysfunction' Basic Res Cardiol; vol. 103, no. 5, pp. 398-406.

Paper Citation

in Harvard Style

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

in Bibtex Style

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)},

in EndNote Style

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