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AMI Screening Using Linguistic Fuzzy Rules

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Abstract

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.

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References

  1. The World Health Organization, The World Health Report 2002. Accessed February 12, 2010, from http://www.who.int/whr/2002/en.

  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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  11. Baxt, W. G., and Skora, J., Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 347:12–15, 1996.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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. Zahan, S., A fuzzy approach to computer-assisted myocardial ischemia diagnosis. Artif. Intell. Med. 21(1):271–275, 2001.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  22. Engin, M., ECG, beat classification using neuro-fuzzy network. Pattern Recogn. Lett. 25:1715–1722, 2004.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

  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. Osowski, S., and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48(11):1265–1271, 2001.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  29. Osowski, S, and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48:1265–71, 2001.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  33. Casillas, J., Cordon, O., Herrera, F., and Magdalena, L., (Eds.), Interpretability Issues in Fuzzy Modeling. Heidelberg: Springer, 2003.

    MATH  Google Scholar 

  34. Dubois, D., and Prade, H., What are fuzzy rules and how to use them. Fuzzy Sets Syst. 84:169–185, 1996.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  36. Nauck, D., Data Analysis with Neuro Fuzzy Methods Habilitation thesis. Otto-von-Guericke University of Magdeburg, Faculty of Computer Science, Magdeburg, Germany, 2000.

  37. Bardossy, A., The use of fuzzy rules for the description of elements in the hydrological cycle. Ecol. Model. 85:3–12, 1996.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  43. Srinivas, N., and Deb, K., Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2:221–248, 1994.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  45. Dash, M., and Liu, H., Feature Selection for Classification Intelligent Data Analysis. Vol. 1, pp. 131–156, 1997.

  46. Mamdani, E. H., Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. 26(12):1182–1191, 1977.

    Article  MATH  Google Scholar 

  47. Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.

    MATH  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

  50. Altman, D. G., and Bland, J. M., Diagnostic tests, 3: receiver operating characteristic plots. Br. Med. J. 309:188, 1994.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Piramuthu, S., Financial credit-risk evaluation with neural and neurofuzzy systems. Eur. J. Oper. Res. 112:310–321, 1999.

    Article  Google Scholar 

  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 

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Acknowledgements

This research is supported by a fundamental research grant scheme from Ministry of Higher Education, Malaysia.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Raja Noor Ainon & Adel Lahsasna

  2. Julius Centre, Faculty of Medicine, CRYSTAL, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Awang M. Bulgiba

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  1. Raja Noor Ainon
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Correspondence to Raja Noor Ainon.

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Ainon, R.N., Bulgiba, A.M. & Lahsasna, A. AMI Screening Using Linguistic Fuzzy Rules. J Med Syst 36, 463–473 (2012). https://doi.org/10.1007/s10916-010-9491-2

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