Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Expert Systems

Volumn 24, Issue 3, 2007, Pages 131-142

  Süt, Necdet ^a   Şenocak, Mustafa ^b  

^a TRAKYA UNIVERSITY   (Turkey)

^b ISTANBUL UNIVERSITY   (Turkey)

Author keywords

Coronary artery disease; Logistic regression; Multilayer perceptron; Neural networks; Training algorithms

Indexed keywords

BACKPROPAGATION; DECISION MAKING; DIAGNOSIS; DISEASES; REGRESSION ANALYSIS;

CORONARY ARTERY DISEASE; LOGISTIC REGRESSION; MARQUARDT ALGORITHMS; TRAINING ALGORITHMS;

MULTILAYER NEURAL NETWORKS;

EID: 34250201125     PISSN: 02664720     EISSN: 14680394     Source Type: Journal    
DOI: 10.1111/j.1468-0394.2007.00425.x     Document Type: Article

References (39)

1. Andres, J., M. Landajo P. Lorca (2005) Forecasting business profitability by using classification techniques: a comparative analysis based on a Spanish case, European Journal of Operational Research, 167, 518 542.
2. Battiti, R. (1992) First- and second-order methods for learning: between steepest descent and Newton's method, Neural Computation, 4, 141 166.
3. Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Oxford : Oxford University Press.
4. Croux, C. K. Joossens (2005) Influence of observations on the misclassification probability in quadratic discriminant analysis, Journal of Multivariate Analysis, 96 (2), 384 403.
5. Dawson-Saunders, B. R.G. Trapp (1993) Basic and Clinical Biostatistics, 2nd edn, Norwalk, CT : Appleton & Lange.
6. Duda, R.O., P.E. Hart D.G. Strok (2001) Pattern Classification, New York : Wiley.
7. Elman, J.L. (1990) Finding structure in time, Cognitive Science, 14, 179 211.
8. Fahlman, S.E. (1988) An empirical study of learning speed in backpropagation networks, Computer Science Technical Report CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, PA.
9. Gazzaruso, C., R. Bruno, A. Pujia, E. De Amici, P. Fratino, S.B. Solerte A. Garzaniti (2006) Lipoprotein (a), apolipoprotein (a) polymorphism and coronary atherosclerosis severity in type 2 diabetic patients, International Journal of Cardiology, 108 (3), 354 358.
10. Güler, I. E.D. Übeyli (2005) An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks, Expert Systems, 22 (2), 62 71.
11. Güler, I. E.D. Übeyli (2006a) A recurrent neural network classifier for Doppler ultrasound blood flow signals, Pattern Recognition Letters, 27, 1560 1571.
12. Güler, I. E.D. Übeyli (2006b) Diabetes diagnosis by multilayer perceptron neural networks, Journal of the Faculty of Engineering and Architecture of Gazi University, 21 (2), 319 326.
13. Güler, N.F., E.D. Übeyli I. Güler (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification, Expert Systems with Applications, 29 (3), 506 514.
14. Gulledge, J. S. Beard (1999) Coronary Artery Disease and Related Conditions Management: Clinical Pathways, Guidelines, and Patient Education, Aspen Chronic Disease Management Series, Gaithersburg, MD: Health and Administration Development Group, Aspen.
15. Gurgen, F., N. Güler F. Varol (2000) Antenatal fetal risk assessment by blood-flow velocity waveforms, IEEE Engineering in Medicine and Biology, 19 (6 88 93.
16. Hagan, M.T. M.B. Menhaj (1994) Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5 (6), 989 993.
17. Hand, D.J. (1997) Construction and Assessment of Classification Rules, New York : Wiley.
18. Hennekens, C.H. (1998) Increasing burden of cardiovascular disease: current knowledge and future directions for research on risk factors, Circulation, 97, 1095 1102.
19. Hennekens, C.H. (2000) Clinical and research challenges in risk factors for cardiovascular diseases, European Heart Journal, 21, 1917 1921.
20. Hennekens, C.H., A.R. Hennekens, D. Hollar D.E. Casey (2005) Schizophrenia and increased risks of cardiovascular disease, American Heart Journal, 150 (6), 1115 1121.
21. Ho, S.H., S.H. Jee, J.E. Lee J.S. Park (2004) Analysis on risk factors for cervical cancer using induction technique, Expert Systems with Applications, 27 (1), 97 105.
22. Icer, S., S. Kara A. Güven (2005) Comparison of multilayer perceptron training algorithms for portal venous Doppler signals in the cirrhosis disease, Expert Systems with Applications, 31 (2), 406 413.
23. Jacobs, R.A. (1988) Increased rate of convergence through learning rate adaptation, Neural Networks, 1, 295 307.
24. Kong, L.X., P.D. Hodgson D.C. Collinson (1998) Modelling the effect of carbon content on hot strength of steels using a modified artificial neural network, IJIJ International, 38 (10), 1121 1129.
25. Makhoul, J. (1990) Artificial neural networks, Investigative Radiology, 25 (6), 748 750.
26. Minai, A.A. R.D. Williams (1990) Back-propagation heuristics: a study of the extended delta-bar-delta algorithm, Proceedings of an International Joint Conference on Neural Networks, San Diego, CA, Vol. 1, pp. 595 600.
27. Motulsky, H. (1995) Intuitive Biostatistics, Oxford : Oxford University Press.
28. Newell, M.C., C.R. Henry, C.J.G. Sigakis, B.T. Unger, D.M. Larson, I.J. Chavez, M.N. Burke, J.H. Traverse T.D. Henry (2006) Comparison of safety and efficacy of sirolimus-eluting stents versus bare metal stents in patients with st-segment elevation myocardial infarction, American Journal of Cardiology, 97 (9), 1299 1302.
29. Petrosian, A.A., D.V. Prokhorov, W. Lajara-Nanson R.B. Schiffer (2001) Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG, Clinical Neurophysiology, 112 (8), 1378 1387.
30. Pineda, F.J. (1987) Generalization of back-propagation to recurrent neural networks, Physical Review Letters, 59 (19), 2229 2232.
31. Ripley, B.D. (1996) Pattern Recognition and Neural Networks, Cambridge : Cambridge University Press.
32. Rumelhart, D., G. Hinton R. Williams (1986) Learning internal representations by backpropagating errors, in D. Rumelhart J. McClelland (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Boston, MA : MIT Press, Vol. 1, pp. 318 362.
33. Simon, H. (1994) Neural Networks, New York : Macmillan.
34. Statsoft Electronic Textbook (2006) URL :, access date 3 November 2006.
35. Turkoglu, I., A. Arslan I. Erdogan (2003) A wavelet neural network for the detection of heart valve diseases, Expert Systems, 20 (1), 1 7.
36. Wald, M., A.D. Seftel, L.S. Ross, M.A.B. Mohamed C.S. Niederberger (2005) Computational models for detection of erectile dysfunction, Journal of Urology, 173 (1), 167 170.
37. Yan, H., Y. Jiang, J. Zheng, C. Peng Q. Li (2006) A multilayer perceptron-based medical decision support system for heart disease diagnosis, Expert Systems with Applications, 30, 272 281.
38. Yorganci, S. (1999) Koroner arter hastaligi saptanan 1049 erkek hastada lipid dagilimi, MD thesis, Istanbul University, Cerrahpasa Medical Faculty.
39. Zhong, X. (2004) A study of several statistical methods for classification with application to microbial source tracking, Master's thesis, Worcester Polytechnic Institute.