JSEA  Vol.7 No.12 , November 2014
Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches
ABSTRACT
This paper aims to design and implement an automatic heart disease diagnosis system using MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing, where 80% and 20% of the Cleveland data set were randomly selected for training and testing purposes respectively. Each system also has an additional module known as case-based module, where the user has to input values for 13 required attributes as specified by the Cleveland data set, in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.

Cite this paper
Abushariah, M. , Alqudah, A. , Adwan, O. and Yousef, R. (2014) Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches. Journal of Software Engineering and Applications, 7, 1055-1064. doi: 10.4236/jsea.2014.712093.
References
[1]   Gudadhe, M., Wankhade, K. and Dongre, S. (2010) Decision Support System for Heart Disease based on Support Vector Machine and Artificial Neural Network. IEEE International Conference on Computer and Communication Technology, Allahabad, 17-19 September 2010, 741-745.

[2]   Yan, H.M., Jiang, Y.T., Zheng, J., Peng, C.L. and Li, Q.H. (2006) A Multilayer Perceptron-Based Medical Decision Support System for Heart Disease Diagnosis. Expert Systems with Applications, 30, 272-281. http://dx.doi.org/10.1016/j.eswa.2005.07.022

[3]   Palaniappan, S. and Awang, R. (2008) Intelligent Heart Disease Prediction System Using Data Mining Techniques. International Journal of Computer Science and Network Security, 8, 343-350.

[4]   Wu, R., Peters, W. and Morgan, M.W. (2002) The Next Generation Clinical Decision Support: Linking Evidence to Best Practice. Journal Healthcare Information Management, 16, 50-55.

[5]   Adeli, A. and Neshat, M. (2010) A Fuzzy Expert System for Heart Disease Diagnosis. Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong, 17-19 March 2010.

[6]   Sivanandam, S.N., Sumathi, S. and Deepa, S.N. (2006) Introduction to Neural Networks Using MATLAB 6.0. McGraw-Hill Education, New York City.

[7]   Kahramanli, H. and Allahverdi, N. (2008) Design of a Hybrid System for the Diabetes and Heart Diseases. Expert Systems with Applications, 35, 82-89.
http://dx.doi.org/10.1016/j.eswa.2007.06.004

[8]   Das, R., Turkoglu, I. and Sengur, A. (2009) Effective Diagnosis of Heart Disease through Neural Networks Ensembles. Expert Systems with Applications, 36, 7675-7680.
http://dx.doi.org/10.1016/j.eswa.2008.09.013

[9]   Allahverdi, N., Torun, S. and Saritas, I. (2007) Design of a Fuzzy Expert System Determination of Coronary Heart Disease Risk. Proceedings of International Conference on Computer Systems and Technologies, Rousse, June 14-15 2007, 1-8.

[10]   Lisboa, P.J. (2002) A Review of Evidence of Health Benefit from Artificial Neural Networks in Medical Intervention. Neural Networks, 15, 11-39. http://dx.doi.org/10.1016/S0893-6080(01)00111-3

[11]   Fuller, R. (1995) Neural Fuzzy Systems. Abo Akademi University, Turku.

[12]   Tung, W.L. and Quek, C. (2002) GenSoFNN: A Generic Self-Organizing Fuzzy Neural Network. Proceedings of IEEE Transactions on Neural Networks Conference, 13, 1075-1086.

[13]   Alhanafy, T.E., Zaghlool, F. and El Din Moustafa, A.S. (2010) Neuro-Fuzzy Modeling Scheme for the Prediction of Air Pollution. Journal of American Science, 6, 605-616.

[14]   Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665-685. http://dx.doi.org/10.1109/21.256541

[15]   KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problem.
http://sci2s.ugr.es/keel/dataset.php?cod=57

[16]   Trewartha, D. (2006) Investigating Data Mining in MATLAB. Bachelor Dissertation, Department of Science, Rhodes University, Grahamstown.

[17]   Harrison, R. (2000) Decision Support Technique Developed in MATLAB Improves the Accuracy of Medical Diagnoses. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield.

[18]   Zhao, L., Zheng, X.Q. and Wang, S.Q. (2008) Design and Implementation of Spatial Data Mining System (M-SDM) Based on MATLAB. Journal of Computers, 3, 66-70.

[19]   Gan, G.J., Ma, C.Q. and Wu, J.H. (2007) Data Clustering: Theory, Algorithms and Applications. ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, ASA, Alexandria.

 
 
Top