Diagnosis of Coronary Artery Disease via a Novel Fuzzy Expert System Optimized by Cuckoo Search

Authors

1 Department of Computer Engineering, University of Bojnord, Bojnord, Iran

2 Department of Computer Engineering, Kosar University of Bojnord, Bojnord, Iran

Abstract

In this paper, we propose a novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm. This system includes three phases: firstly, at the stage of fuzzy system design, a decision tree is used to extract if-then rules which provide the crisp rules required for Coronary Artery Disease detection. Secondly, the fuzzy system is formed by setting the intervals for fuzzy variables and extracted rules. Finally, Cuckoo Search algorithm is used to optimize fuzzy membership functions. The accuracy of our proposed system is evaluated using Cleveland Cardiac Patient Database. The detection rate is 93.48% employing optimized membership functions. Also, 85.76% accuracy is obtained for predicting the risk of coronary artery disease. The superiority of proposed system is obvious by comparing it to the previously methods; it is more accurate and is also easier to implement.

Keywords

References

1.     Wong, N.D., “Epidemiological studies of CHD and the evolution of preventive cardiology”, Nature Reviews Cardiology,  Vol. 11, No. 5, (2014), 276–289.
2.     Buchan, K., Filannino, M., and Uzuner, Ö., “Automatic prediction of coronary artery disease from clinical narratives”, Journal of biomedical informatics,  Vol. 72, (2017), 23–32.
3.     Davari Dolatabadi, A., Khadem, S.E.Z., and Asl, B.M., “Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM”, Computer Methods and Programs in Biomedicine,  Vol. 138, , (2017), 117–126.
4.     Ramezankhani, A., Bagherzadeh-Khiabani, F., Khalili, D., Azizi, F., and Hadaegh, F., “A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study”, Scientific Reports,  Vol. 7, No. 3237, (2017), 1-11.
5.     Cagle, S.D., and Cooperstein, N., “Coronary Artery Disease: Diagnosis and Management”, Primary care,  Vol. 45, No. 1, (2018), 45–61.
6.     Shouman, M., Turner, T., and Stocker, R., “Using decision tree for diagnosing heart disease patients”, In Proceedings of the Ninth Australasian Data Mining Conference - Volume 121, Australian Computer Society, (2011), 23–30.
7.     Leach, H.J., O’Connor, D.P., Simpson, R.J., Rifai, H.S., Mama, S.K., and Lee, R.E., “An exploratory decision tree analysis to predict cardiovascular disease risk in African American women”, Health Psychology,  Vol. 35, No. 4, (2016), 397–402.
8.     El-Bialy, R., Salamay, M.A., Karam, O.H., and Khalifa, M.E., “Feature Analysis of Coronary Artery Heart Disease Data Sets”, Procedia Computer Science,  Vol. 65, (2015), 459–468.
9.     Polat, K., Şahan, S., and Güneş, S., “Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing”, Expert Systems with Applications,  Vol. 32, No. 2, (2007), 625–631.
10.   Dennis, B., and Muthukrishnan, S., “AGFS: Adaptive Genetic Fuzzy System for medical data classification”, Applied Soft Computing,  Vol. 25, (2014), 242–252.
11.   Nguyen, T., Khosravi, A., Creighton, D., and Nahavandi, S., “Medical data classification using interval type-2 fuzzy logic system and wavelets”, Applied Soft Computing,  Vol. 30, No. C, (2015), 812–822.
12.   Nguyen, T., Khosravi, A., Creighton, D., and Nahavandi, S., “Classification of healthcare data using genetic fuzzy logic system and wavelets”, Expert Systems with Applications,  Vol. 42, No. 4, (2015), 2184–2197.
13.   Anooj, P.K., “Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules”, Journal of King Saud University - Computer and Information Sciences,  Vol. 24, No. 1, (2012), 27–40.
14.   Marateb, H.R., and Goudarzi, S., “A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system”, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences,  Vol. 20, No. 3, (2015), 214–223.
15.   Baihaqi, W.M., Setiawan, N.A., and Ardiyanto, I., “Rule extraction for fuzzy expert system to diagnose Coronary artery disease”, In 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, (2016), 136–141.
16.   Safdarian, N., Dabanloo, N., and Attarodi, G., “A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal”, Journal of Biomedical Science and Engineering,  Vol. 07, No. 10, (2014), 818–824.
17.   Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., and Yarifard, A.A., “Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm”, Computer Methods and Programs in Biomedicine,  Vol. 141, (2017), 19–26.
18.   Paul, A.K., Shill, P.C., Rabin, M.R.I., and Murase, K., “Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease”, Applied Intelligence,  Vol. 48, No. 7, (2018), 1739–1756.
19.   Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Sandhu, S., and Guppy, K., “International application of a new probability algorithm for the diagnosis of coronary artery disease”, The American journal of cardiology,  Vol. 64, No. 5, (1989), 304–310.
20.   Schafer, J.L., and Graham, J.W., “Missing Data: Our View of the State of the Art”, Psychological Methods,  Vol. 07, No. 2, (2002), 147–177.
21.   Patil, B.M., Joshi, R.C., and Toshniwal, D., “Missing Value Imputation Based on K-Mean Clustering with Weighted Distance”, In International Conference on Contemporary Computing, Springer, Berlin, Heidelberg, (2010), 600–609.
22.   Rokach, L., and Maimon, O., Data mining with decision trees: theory and applications, Vol. 69. World scientific, (2008).
23.   Wang, W., Li, Q., Han, S., and Lin, H., “A Preliminary Study on Constructing Decision Tree with Gene Expression Programming”, In First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC’06), IEEE, (2006), 222–225.
24.   Mahdizadeh, M., and Eftekhari, M., “A Novel Cost Sensitive Imbalanced Classification Method based on New Hybrid Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms”, International Journal of Engineering - Transactions B: Applications,  Vol. 28, No. 8, (2015), 1160–1168.
25.   Moaref, A., and Sattari-Naeini, V., “Fuzzy-rough Information Gain Ratio Approach to Filter-wrapper Feature Selection”, International Journal of Engineering - Transactions C: Aspects,  Vol. 30, No. 9, (2017), 1326–1333.
26.   Takagi, T., and Sugeno, M., “Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions on Systems, Man, and Cybernetics,  Vol. SMC-15, No. 1, (1985), 116–132.
27.   Yulianto, T., Komariyah, S., and Ulfaniyah, N., “Application of fuzzy inference system by Sugeno method on estimating of salt production”, In AIP Conference Proceedings, AIP Publishing LLC, (2017).
28.   Muthukaruppan, S., and Er, M.J., “A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease”, Expert Systems with Applications,  Vol. 39, No. 14, (2012), 11657–11665.
29.   Bazoobandi, H.A., and Eftekhari, M., “A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network”, International Journal of Engineering - Transactions B: Applications,  Vol. 27, No. 8, (2014), 1185–1194.
30.   Yang, X.-S., and Suash Deb, S., “Cuckoo Search via Lévy flights”, In World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, (2009), 210–214.
31.   Payne, R., and Sorensen, M., The cuckoos, Vol. 15. Oxford University Press, (2005).
32.   Yang, X.-S., Nature-inspired metaheuristic algorithms, Luniver Press, (2008).
33.   Yang, X.-S., and Deb, S., “Engineering Optimisation by Cuckoo Search”, International Journal of Mathematical Modelling and Numerical Optimisation,  Vol. 01, No. 4, (2010), 330–343.
34.   Samadiani, N., and Moameri, S., “Diagnosis of Coronary Artery Disease using Cuckoo Search and genetic algorithm in single photon emision computed tomography images”, In 7th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, (2017), 314–318.
35.   Yildiz, A.R., “Cuckoo search algorithm for the selection of optimal machining parameters in milling operations”, The International Journal of Advanced Manufacturing Technology,  Vol. 64, No. 1–4, (2013), 55–61.
International Journal of Engineering
Volume 31, Issue 12
TRANSACTIONS C: Aspects
December 2018
Pages 2028-2036

Files

Share

How to cite

Statistics