Abstract
In this paper, a Bayesian (belief) network with fuzzy probabilities is proposed for heart disease diagnosis. Due to the complexity of relations between the features we used the Bayesian belief network. The fuzzy probabilities are also used because of the multiplicity of initial probability and belonging each of features to their related class. We have used the classification methods for determining the heart diseases class. For depicting the Bayesian network, we applied the K2 algorithm. We comprised the results of our network with the result of the Bayesian network, naive Bayesian, multi-Support vector machine, multilayer perceptron, radial basis function, and k-nearest neighbors. The result showed that our model is more accurate than others.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barr, A., Feigenbaum, E., Roads, C.: The Handbook of Artificial Intelligence, vol. 1, p. 78 (1982)
Soni, J., Ansari, U., Sharma, D., Soni, S.: Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011)
Rajkumar, A., Reena, G.S.: Diagnosis of heart disease using datamining algorithm. Glob. J. Comput. Sci. Technol. 10(10), 38–43 (2010)
Srinivas, K., Rao, G.R., Govardhan, A.: Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques. In: 2010 5th International Conference on 2010 Computer Science and Education (ICCSE), pp. 1344–1349. IEEE, 24 August 2010
Xing, Y., Wang, J., Zhao, Z.: Combination data mining methods with new medical data to predicting outcome of coronary heart disease. In: 2007 International Conference on 2007 Convergence Information Technology, pp. 868–872. IEEE, 21 November 2007
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
McKendrick, I.J., Gettinby, G., Gu, Y., Reid, S.W., Revie, C.W.: Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases. Prev. Vet. Med. 47(3), 141–156 (2000)
Heckerman, D.E., Horvitz, E.J., Nathwani, B.N.: Toward normative expert systems: the Pathfinder project. Methods Inf. Med. 31, 90I105 (1991)
Li, C., Ueno, M.: An extended depth-first search algorithm for optimal triangulation of Bayesian networks. Int. J. Approx. Reason. 31(80), 294–312 (2017)
Díez, F.J., Mira, J., Iturralde, E., Zubillaga, S.: DIAVAL, a Bayesian expert system for echocardiography. Artif. Intell. Med. 10(1), 59–73 (1997)
Shwe, M.A., Middleton, B., Heckerman, D.E., Henrion, M., Horvitz, E.J., Lehmann, H.P., Cooper, G.F.: Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Methods Inf. Med. 30(4), 241–255 (1991)
Pradhan, M., Provan, G., Middleton, B., Henrion, M.: Knowledge engineering for large belief networks. In: Proceedings of 10th International Conference on Uncertainty in Artificial Intelligence, 29 July 1994, pp. 484–490. Morgan Kaufmann Publishers Inc. (1994)
Li, Y.C.: Automated probabilistic transformation of a large medical diagnostic support system (1995)
Matzkevich, I., Abramson, B.: Decision analytic networks in artificial intelligence. Manag. Sci. 41(1), 1–22 (1995)
Henrion, M., Cooley, D.R.: An experimental comparison of knowledge engineering for expert systems and for decision analysis. In: Proceedings of 6th National Conference on AI (AAAI-1987), Seattle, WA, pp. 471–476 (1987)
Kalagnanam, J., Henrion, M.: A comparison of decision analysis and expert rules for sequential diagnosis. arXiv preprint arXiv:1304.2362 (2013)
Wise, B.P., Henrion, M.: A framework for comparing uncertain inference systems to probability. In: Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence, pp. 69–83. Elsevier, Amsterdam (1986)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Ryhajlo, N., Sturlaugson, L., Sheppard, J.W.: Diagnostic Bayesian networks with fuzzy evidence. In: 2013 IEEE AUTOTESTCON, pp. 1–8. IEEE (2013)
Okuda, T., Tanaka, H., Asai, K.: A formulation of fuzzy decision problems with fuzzy information using probability measures of fuzzy events. Inf. Control 38(2), 135–147 (1978)
Tanaka, H., Okuda, T., Asai, K.: Fuzzy information and decision in statistical model. Adv. Fuzzy Set Theory Appl. 303–320 (1979)
Uemura, Y.: A decision rule on fuzzy events. Japan. J. Fuzzy Theory Syst. 3, 291–300 (1991)
Viertl, R., Hule, H.: On Bayes’ theorem for fuzzy data. Stat. Pap. 32(1), 115–122 (1991)
Viertl, R.: Statistical Methods for Non-precise Data. CRC Press, Boca Raton (1995)
Tang, H., Liu, S.: Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 24 August 2007, vol. 4, pp. 132–137. IEEE (2007)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Burlington (2014)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Fazel Zarandi, M.H., Seifi, A., Ershadi, M.M., Esmaeeli, H. (2018). An Expert System Based on Fuzzy Bayesian Network for Heart Disease Diagnosis. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-67137-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67136-9
Online ISBN: 978-3-319-67137-6
eBook Packages: EngineeringEngineering (R0)