Fuzzy Logic System for Diagnosing Coronary Heart Disease

Authors

  • Mustafa Salah Mahdi
  • Mohd Faisal Ibrahim
  • Shakir Mahmood Mahdi
  • Praveen Singam
  • Aqilah Baseri Huddin

DOI:

https://doi.org/10.14419/ijet.v8i1.7.25966

Published:

2019-01-18

Keywords:

fuzzy logic, expert system, coronary heart disease (CHD), computer-aided diagnostic system (CAD), health care,

Abstract

The diagnosis of coronary heart disease is a non-trivial task that requires a careful and time-consuming examination. Hence, the application of a computer-aided diagnostic (CAD) system to assess the condition of a person of having coronary heart disease is greatly beneficial. Although the usage of CADs related to coronary heart disease diagnosis is widely implemented, the inputs medical data required is still a significant challenge to encourage rapid diagnosis among public. This paper presents a fuzzy logic system for diagnosing coronary heart disease. In this study, the fuzzy logic based CAD system has been developed with five input variables that requires minimal medical procedure to obtain to determine the presence of coronary heart disease. Experiments to assess the accuracy of the system and comparison with a previous work of rough-fuzzy classifier were carried out using an open source coronary heart disease dataset. The results show that the proposed work is able to achieve significant accuracy of 72.6%, an improvement of 30% from the previous work.

 

 

References

[1] Benjamin EJ et al., “Heart Disease and Stroke Statistics – 2018 Update A Report From the American Heart Associationâ€, Circulation Vol. 137 No. 12, pp. e67-e492, 2018.

[2] World Health Organization, “Noncommunicable diseases country profiles 2018â€, Geneva, 2018.

[3] Parvin R, Abhari A, “Fuzzy database for heart disease diagnosisâ€, Proceedings of Medical Processes Modeling and Simulation (MPMS), 2012.

[4] Farlex. (Ed.), Medical Dictionary, 2009.

[5] Innocent PR., John, RI, Garibaldi, JM, “Fuzzy methods for medical diagnosisâ€, Applied Artificial Intelligence, Vol. 19 No. 12, pp. 69-98, 2004.

[6] Hemba S, Islam, N, “Fuzzy Logic: A Reviewâ€, International Journal of Computer Sciences and Engineering, Vol. 5 No. 2, 61-63, 2017.

[7] Thakur S, Raw SN, Sharma R, “Design of a Fuzzy Model for Thalassemia Disease Diagnosis: Using Mamdani Type Fuzzy Inference System (FIS)â€, International Journal of Pharmacy and Pharmaceutical Sciences, Vol. 8 No. 4, 2016.

[8] Zhenning Y, Vijayashree J, Jayashree J, “Fuzzy logic based diagnosis for liver disease using CBC (Complete Blood Counts)â€, J. Comput Math Sci, Vol. 8, pp. 202-209, 2017.

[9] Asogbon MG, Samuel OW, Omisore MO, Awonusi O, “Enhanced neuro-fuzzy system based on genetic algorithm for medical diagnosisâ€, J Med Diagn Meth, Vol. 5 No. 205, 2016.

[10] Abidin B, Dom RM, Rahman ARA, Bakar RA, Demiralp M, Baykara N, Mastorakis N, “Use of fuzzy neural network to predict coronary heart disease in a Malaysian sampleâ€, Proceedings 8th WSEAS International Conference on Telecommunications and Informatics, 2009, pp. 76-80.

[11] Adeli A, Neshat M. “A fuzzy expert system for heart disease diagnosisâ€, Proceedings International Multi Conference of Engineers and Computer Scientists, 2010.

[12] Cinetha K, Maheswari PU, “Decision Support System for Precluding Coronary Heart Disease (CHD) Using Fuzzy Logicâ€, International Journal of Computer Science Trends and Technology, Vol. 2 No. 2, pp. 2347-857, 2014.

[13] Hassan N, Sayed OR, Khalil AM, Ghany MA, “Fuzzy soft expert system in prediction of coronary artery diseaseâ€, International Journal of Fuzzy Systems, Vol. 19 No. 5, pp. 1546-1559, 2017.

[14] Srinivas K, Rao GR, Govardhan A, “Rough-Fuzzy classifier: A system to predict the heart disease by blending two different set theoriesâ€, Arabian Journal for Science and Engineering, Vol. 39 No. 4, pp. 2857-2868, 2014.

[15] Bache K, Lichman M, UCI Machine Learning Repository [http://archive. ics. uci.edu/ml]. University of California, School of Information and Computer Science. Irvine, CA, 2013.

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