Fuzzy Logic in a Decision Support System in the Domain of Coronary Heart Disease Risk Assessment

  • Alfons Schuster
  • Kenneth Adamson
  • David A. Bell
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)


Every day humans are confronted in numerous occasions with tasks that include the management and the processing of information of various degrees of complexity. Regardless of what the actual information consists of, its degree of complexity, or simplicity, can be associated with the number of recognised parts and the extent of their interrelationship (Klir and Folger 1988). The capability to manage such information considerably depends on the actual understanding of the person(s) involved. The more experienced the person the better the understanding and the information management. Further, although different persons may approach the same problem differently a solution is very often based on a combination of different strategies. This paper has a focus on two strategies:
  • First, a very common way of managing complex information for domain experts, or humans in general, is to reduce the complexity of the information by allowing a certain degree of uncertainty without loosing the actual content of the original information. In a very natural, but also radical way, complexity reduction occurs when humans summarise information onto vague linguistic expressions. For example, a clinician may say to a person: “Your blood pressure is ok, your heart rate is just fine, and your cholesterol values are normal”. Note that despite the availability of precise values for blood pressure, heart rate and cholesterol the clinician uses the vague linguistic terms ok, just fine and normal to describe the person’s state of health. These terms however are expressive and satisfactory for further decision-making (Ross TJ 1995). Fuzzy logic is a technique that, in many situations, may provide a solution for the modelling of such situations (Zadeh 1996).


Weight Vector Domain Expert Respiratory Sinus Arrhythmia Weight Assignment Direct Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aamodt A and Plaza E (1994) Case-based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AICOM, (vol)7:1:39–59Google Scholar
  2. 2.
    Anderson KM, Wilson PWF, Odell PM and Kannel WB (1991) An Updated Coronary Risk Profile. Circulation (AHA Medical/Scientific Statement) (vol)83:1:356–362Google Scholar
  3. 3.
    Bonissone PP (1985) Editorial: Reasoning with uncertainty in expert systems. Int. Journal Man-Machine Studies 22:241–250CrossRefGoogle Scholar
  4. 4.
    Brown M (1992) Case-Based Reasoning: principles and potential. AI Intelligence, JanuaryGoogle Scholar
  5. 5.
    Cox ED (1995) Fuzzy Logic for Business and Industry. Charles River Media, Rockland, MassachusettsGoogle Scholar
  6. 6.
    Dawber TR, Meadors GF and Moore FEJ (1951) Epidemiological approaches to heart disease: the framingham study. Am J Public Health 41:279–286CrossRefGoogle Scholar
  7. 7.
    Goldberg DE (1989) Genetic Algorithm in Search. Optimization, and Machine Learning. Addison WesleyGoogle Scholar
  8. 8.
    Gordon DJ, Probstfield JL and Garrison RJ (1989) High density lipoprotein cholesterol and cardiovascular disease. Circulation (vol)79:8:8–15PubMedCrossRefGoogle Scholar
  9. 9.
    Grefenstette JJ (1986) Optimization of control parameters for denetic algorithms. In: Buckles BP, Petry FE (eds) Genetic Algorithms. IEEE Computer Society Press, Los Alamos, California, pp 5–11Google Scholar
  10. 10.
    Hopkins PN and Williams RR (1981) A survey of 246 suggested coronary risk factors. Atherosclerosis 40:1–52PubMedCrossRefGoogle Scholar
  11. 11.
    Jones EK and Roydhouse A (1995) Intelligent retrieval of archived meteorological data. IEEE Expert Intelligent Systems and their Applications, pp 50–57Google Scholar
  12. 12.
    Kannel WB, Feinleib M, McNamara PM, Garrison RJ and Castelli WP (1979) An investigation of coronary heart disease in families: the Framingham offspring study. Am J Epideniol 110:281–290Google Scholar
  13. 13.
    Kinosian B, Glick H and Garland G (1994) Cholesterol and coronary heart disease — predicting risks by levels and ratios. Annals of Internal Medicine (vol)121:9:641–647PubMedGoogle Scholar
  14. 14.
    Klir GJ and Folger TA (1988) Fuzzy Sets, Uncertainty and Information. Prentice Hall, Englewood Cliffs, New JerseyGoogle Scholar
  15. 15.
    Kolodner J (1993) Case-Based Reasoning. Morgan Kaufmann, San Mateo, CaliforniaGoogle Scholar
  16. 16.
    Levy D (1993) A multifactorial approach to coronary disease risk assessment. Clin. And Exper. Hypertension (vol)15:6:1077–1086CrossRefGoogle Scholar
  17. 17.
    Lopes P, White JA and Anderson J (1997) Decreased respiratory sinus arrhythmia as an indicator of coronary heart disease risk in middle-aged males. Medical and Biological Engineering and Computing (vol)35:1:578Google Scholar
  18. 18.
    Lopes PL, Mitchell RH and White JA (1994) The relationships between respiratory sinus arrhythmia and coronary heart disease risk factors in middleaged males. Automedica, 16:71–76Google Scholar
  19. 19.
    Mitchell M (1996) An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts, LondonGoogle Scholar
  20. 20.
    Riesbeck CK and Schank RC (1989) Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Hillsdale, New JerseyGoogle Scholar
  21. 21.
    Ross R (1986) The pathogenesis of atherosclerosis: an update. New Engl J. Med. 314:488–500PubMedCrossRefGoogle Scholar
  22. 22.
    Ross TJ (1995) Fuzzy Logic with Engineering Applications. New York; London, McGraw-HillGoogle Scholar
  23. 23.
    Schneider M and Kandel A (1992) General purpose fuzzy expert systems. In: Kandel A (ed) Fuzzy Expert Systems. CRC Press, Boca Raton, Florida, pp 23–41Google Scholar
  24. 24.
    Schuster A, Adamson K and Bell DA (1999) Generating summaries from retrieved base cases. Workshop on Data Analysis in Medicine and Pharmacology IDAMAP’99, Washington DC, USA, pp 117–122Google Scholar
  25. 25.
    Schuster A, Dubitzky W, Lopes P, Adamson K, Bell DA, Hughes JG and White JA (1997) Aggregating features and matching cases on vague linguistic expressions. 15th Int. Joint Conference on Artificial Intelligence IJCAI 1997, Nagoya, Japan, pp 252–257Google Scholar
  26. 26.
    Schuster A, Lopes P, Adamson K, Bell DA and White JA (1998a) An application of case-based reasoning in the domain of coronary heart disease risk. Int. ICSC Symposium on Engineering of Intelligent Systems EIS’ 98, University of La Laguna, Tenerife, Spain, pp 469–475Google Scholar
  27. 27.
    Schuster A, Lopes P, Adamson K, Bell DA and White JA (1998b) Intelligent diagnosis through fuzzy expert systems. The World Multiconference on Systemics, Cybernetics and Informatics SCI’ 98 and the 4th Int. Conference on Information System Analysis and Synthesis ISAS’98, Orlando, USA, pp 157–163Google Scholar
  28. 28.
    Shaper AG, Pocock SJ, Phillips AN and Walker M (1987) A scoring system to identity men at high risk of heart attack. Health Trends 19:37–39PubMedGoogle Scholar
  29. 29.
    Slyper AH (1994) Low-density-lipoprotein density and atherosclerosis — unravelling the connection. JAMA (vol)272:4:305–308PubMedCrossRefGoogle Scholar
  30. 30.
    Theorell T (1992) The Psycho-Social Environment, Stress and Coronary Heart Disease. In: Marmott G, Elliot P (eds) Coronary Heart Disease Epidemiology: from Aetiology to Public Health. Oxford University Press, Oxford, UKGoogle Scholar
  31. 31.
    Tunstall-Pedoe H (1991) The Dundee risk-disk for management of change in risk factors, Brit Med J September 303:744–747CrossRefGoogle Scholar
  32. 32.
    Wilensky R (1986) Knowledge representation — A critique and a proposal. In: Kolodner J, Riesbeck K (eds) Experience, Memory, And Reasoning. Lawrence Erlbaum Associates, Hillsdale, New Jersey, pp 15–28Google Scholar
  33. 33.
    Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, May (vol)4:2:103–111Google Scholar
  34. 34.
    Zadeh LA (1973) Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, SMC (vol)3:1:28–45Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alfons Schuster
    • 1
  • Kenneth Adamson
    • 1
  • David A. Bell
    • 1
  1. 1.Faculty of Informatics, School of Information and Software EngineeringUniversity of Ulster at JordanstownNewtownabbey, Co. AntrimNorthern Ireland

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