Journal of Medical Systems

, Volume 36, Issue 2, pp 463–473

AMI Screening Using Linguistic Fuzzy Rules

  • Raja Noor Ainon
  • Awang M. Bulgiba
  • Adel Lahsasna
Original Paper


This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.


AMI Fuzzy rules Prediction system Multiobjective genetic algorithm 


  1. 1.
    The World Health Organization, The World Health Report 2002. Accessed February 12, 2010, from
  2. 2.
    Murray, C. J., and Lopez, A. D., Alternative projections of mortality and disability by cause 19902020: global burden of disease study. Lancet 349:1498–504, 1997.CrossRefGoogle Scholar
  3. 3.
    Williams, W., Thrombolysis after acute myocardial infarction: are Canadian physicians up to the challenge? Can. Med. Assoc. J. 156(4):509–11, 1997.Google Scholar
  4. 4.
    Storrow, A. B., and Gibler, W. B., Chest pain centers: diagnosis of acute coronary syndromes. Ann. Emerg. Med 35:449–61, 2000.Google Scholar
  5. 5.
    Ian, D., Jones, M. D., Corey, M., and Slovis M. D., Pitfalls in evaluating the low-risk chest pain patient. Emerg. Med. Clin. No. Am. 28(1):183–201, 2010.CrossRefGoogle Scholar
  6. 6.
    Lee, T. H., Chest pain in the emergency department: uncertainty and the test of time. Mayo Clin. Proc. 66:963–965, 1999.Google Scholar
  7. 7.
    Bojarczuk, C. C., Lopes, H. S., and Freitas, A. A., Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Eng. Med. Biol. Mag. (Special issue on data mining and knowledge discovery), 19(4):38–44, 2000.CrossRefGoogle Scholar
  8. 8.
    Baxt, W. G., Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115:843–848, 1991 (Erratum in: Ann. Intern. Med. 1992;116:94).Google Scholar
  9. 9.
    Furlong, J. W., Dupuy, M. E., and Heinsimer, J. A., Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence. Am. J. Clin. Pathol. 96:134–141, 1991.Google Scholar
  10. 10.
    Yang, T. F., Devine, B., and Macfarlane, P. W., Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction. J. Electrocardiol. 27 Suppl:188–193, 1994.CrossRefGoogle Scholar
  11. 11.
    Baxt, W. G., and Skora, J., Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 347:12–15, 1996.CrossRefGoogle Scholar
  12. 12.
    Hedn, B., Hlin, H., Rittner, R., and Edenbrandt, L., Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks. Circulation 96:1798–1802, 1997.Google Scholar
  13. 13.
    Ellenius, J., Groth, T., Lindahl, B., and Wallentin, L., Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis. Clin. Chem. 43:1919–1925, 1997.Google Scholar
  14. 14.
    Kennedy, R. L., Harrison, R. F., and Burton, A. M., et al., An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements. Comput. Methods Programs Biomed. 52:93–103, 1997.CrossRefGoogle Scholar
  15. 15.
    Baxt, W. G., Shofer, F. S., Sites, F. D., and Hollander, J. E., A neural computational aid to the diagnosis of acute myocardial infarction. Ann. Emerg. Med. 39:366–373, 2002.CrossRefGoogle Scholar
  16. 16.
    Bulgiba, A., and Fisher, M., Using neural networks and just nine patient-reportable factors of screen for AMI. Health Inform. J. 12:213–225, 2006.CrossRefGoogle Scholar
  17. 17.
    Eggers, K. M., Ellenius, J., Dellborg, M., Groth, T., Oldgren, J., Swahn, E., and Lindahl, B., Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients. Int. J. Cardiol. 114:366–374, 2007.CrossRefGoogle Scholar
  18. 18.
    Conforti, D., and Guido, R., Kernel-based support vector machine classifiers for early detection of myocardial infarction. Optim. Methods Softw. 20(2–3):401–413, 2005.MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Assanelli, D., Cazzamalli, L., Stambini, M., et al., Correct diagnosis of chest pain by an integrated expert system. In: Proc Computers in Cardiology, pp. 759–762. NJ: IEEE, 1993.Google Scholar
  20. 20.
    Zahan, S., A fuzzy approach to computer-assisted myocardial ischemia diagnosis. Artif. Intell. Med. 21(1):271–275, 2001.CrossRefGoogle Scholar
  21. 21.
    Mair, J., Smidt, J., Lechleitner, P., Dienstl, F., and Puschendorf, B., A decision tree for the early diagnosis of acute myocardial infarction in non-traumatic chest pain patients at hospital admission. Chest 108(6):1502–1509, 1995.CrossRefGoogle Scholar
  22. 22.
    Engin, M., ECG, beat classification using neuro-fuzzy network. Pattern Recogn. Lett. 25:1715–1722, 2004.CrossRefGoogle Scholar
  23. 23.
    Lu, H. L., Ong, K., and Chia, P., An automated ECG classification system based on a neuro-fuzzy system. Comput. Cardiol. 27:387–390, 2000.Google Scholar
  24. 24.
    Güler, İ., and Übeyli, E. D., Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Syst. Appl. 27(3):323–330, 2004.CrossRefGoogle Scholar
  25. 25.
    Pilla, V., and Lopes, H. S., Evolutionary training of a neuro-fuzzy network for detection of P wave of the ECG. In: Proceedings of the Third International Conference on Computational Intelligence and Multimedia Applications, pp. 102–106. New Delhi, India, 1999.Google Scholar
  26. 26.
    Engin, M., and Demira, S., Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature set, Cardiovasc. J. Eng. Int. 3(2):71–80, 2003.Google Scholar
  27. 27.
    Osowski, S., and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48(11):1265–1271, 2001.CrossRefGoogle Scholar
  28. 28.
    Özbay, Y., Ceylan, R., and Karlik, B., A fuzzy clustering neural network architecture for classification of ECG arrhytmias. Comput. Biol. Med. 36:376–388, 2006.CrossRefGoogle Scholar
  29. 29.
    Osowski, S, and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48:1265–71, 2001.CrossRefGoogle Scholar
  30. 30.
    Goletsis, Y., Papaloukas, C., Fotiadis, D. I., Likas, A., and Michalis, L. K., Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis. IEEE Trans. Biomed. Eng. 51:171–725, 2004.CrossRefGoogle Scholar
  31. 31.
    Exarchos, T., Tsipouras, M., Exarchos, C., Papaloukas, C., Fotiadis, D., and Michalis, L., A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 40(3)187–200, 2007.CrossRefGoogle Scholar
  32. 32.
    Zeleznikow, J., and Nolan, J. R.. Using soft computing to build real world intelligent decision support systems in uncertain domains. Decis. Support Syst. 31:263–285, 2001.CrossRefGoogle Scholar
  33. 33.
    Casillas, J., Cordon, O., Herrera, F., and Magdalena, L., (Eds.), Interpretability Issues in Fuzzy Modeling. Heidelberg: Springer, 2003.MATHGoogle Scholar
  34. 34.
    Dubois, D., and Prade, H., What are fuzzy rules and how to use them. Fuzzy Sets Syst. 84:169–185, 1996.MathSciNetMATHCrossRefGoogle Scholar
  35. 35.
    Bates, J. H. T., and Young, M. P., Applying fuzzy logic to medical decision making in the intensive care unit. Am. J. Respir. Crit. Care Med. 167:948–952, 2003.CrossRefGoogle Scholar
  36. 36.
    Nauck, D., Data Analysis with Neuro Fuzzy Methods Habilitation thesis. Otto-von-Guericke University of Magdeburg, Faculty of Computer Science, Magdeburg, Germany, 2000.Google Scholar
  37. 37.
    Bardossy, A., The use of fuzzy rules for the description of elements in the hydrological cycle. Ecol. Model. 85:3–12, 1996.CrossRefGoogle Scholar
  38. 38.
    Bulgiba, A. M., Razaz, M., How well can signs and symptoms predict AMI in the Malaysian population? Int. J. Cardiol. 102:87–93, 2005.CrossRefGoogle Scholar
  39. 39.
    Konak, A., Coit, D. W., and Smith, A. E., Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9):992–1007, 2006.CrossRefGoogle Scholar
  40. 40.
    Coello, C. A. C., A comprehensive survey of evolutionary-based multi-objective optimization techniques. Knowl. Inf. Syst. 1(3):269–308, 1999.Google Scholar
  41. 41.
    Van Veldhuizen, D. A., and Lamont, G. B., Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2):125–147, 2000.CrossRefGoogle Scholar
  42. 42.
    Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6:182–197, 2002.CrossRefGoogle Scholar
  43. 43.
    Srinivas, N., and Deb, K., Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2:221–248, 1994.CrossRefGoogle Scholar
  44. 44.
    Deb, K., and Goel, T., Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Zitzler, E., Deb, K., Thiele, L., Coello, C. A. C., and Corne, D., (Eds.), Proceedings of the First International Conference on Evolutionary Multi-Criterion OptimizationEMO 2001. pp. 67–81. Berlin: Springer, 2001.CrossRefGoogle Scholar
  45. 45.
    Dash, M., and Liu, H., Feature Selection for Classification Intelligent Data Analysis. Vol. 1, pp. 131–156, 1997.Google Scholar
  46. 46.
    Mamdani, E. H., Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. 26(12):1182–1191, 1977.MATHCrossRefGoogle Scholar
  47. 47.
    Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.MATHGoogle Scholar
  48. 48.
    Ishibuchi, H., Nakashima, T., and Murata, T., Three objective genetics-based machine leaming for linguistic rule extraction. Inf. Sci. 136(1–4):109–133, 2001.MATHCrossRefGoogle Scholar
  49. 49.
    Kohavi, R., A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Appears in the International Joint Conference on Artificial Inteligence (IJCAI), 195.Google Scholar
  50. 50.
    Altman, D. G., and Bland, J. M., Diagnostic tests, 3: receiver operating characteristic plots. Br. Med. J. 309:188, 1994.CrossRefGoogle Scholar
  51. 51.
    May, R. J., Dandy, G. C., Maier, H. R., and Nixon, J. B., Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ. Model. Softw. 23(10–11):1289–1299, 2008.CrossRefGoogle Scholar
  52. 52.
    Lahsasna, A., Ainon, R. N., and Wah, T. Y., Credit scoring models using soft computing methods: a survey. Int. Arab J. Inf. Technol. 7(2):115–123, 2010.Google Scholar
  53. 53.
    Piramuthu, S., Financial credit-risk evaluation with neural and neurofuzzy systems. Eur. J. Oper. Res. 112:310–321, 1999.CrossRefGoogle Scholar
  54. 54.
    Setnes, M., Simplification and reduction of fuzzy rules. In: Casillas, J., Cordn, O., Herrera, F., and Magdalena, L., (Eds.), Interpretability Issues in Fuzzy Modeling. pp. 278–302. Heidelberg: Springer, 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Raja Noor Ainon
    • 1
  • Awang M. Bulgiba
    • 2
  • Adel Lahsasna
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Julius Centre, Faculty of Medicine, CRYSTAL, Faculty of ScienceUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations