Support vector machine ensembles for intelligent diagnosis of valvular heart disease

Journal of Medical Systems

Volumn 36, Issue 4, 2012, Pages 2649-2655

  Sengur, Abdulkadir ^a  

^a FIRAT UNIVERSITY   (Turkey)

Author keywords

Bagging; Boosting; Doppler heart sounds; Ensemble based method; Random subspaces; Support vector machines; Valvular heart disease

Indexed keywords

ARTICLE; CLASSIFICATION ALGORITHM; COMPARATIVE STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; PARAMETERS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VALVULAR HEART DISEASE; ALGORITHM; COMPUTER ASSISTED DIAGNOSIS; DOPPLER ECHOCARDIOGRAPHY; ECHOGRAPHY; HUMAN;

ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; ECHOCARDIOGRAPHY, DOPPLER; HEART VALVE DISEASES; HUMANS; SUPPORT VECTOR MACHINES;

EID: 84873027513     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9740-z     Document Type: Article

References (26)

1. http://yourtotalhealth.ivillage.com/heart-disease-fast-facts.html, (last accessed: February 13, 2008).
2. http://www.americanheart.org, (last accessed: February 13, 2008.)
3. Keeton, P. I. J., and Schlindwein, F. S. Application of wavelets in Doppler ultrasound. MCB University Press, 17(1):38-45, 1997.
4. Wright, I. A., Gough, N. A. J., Rakebrandt, F., Wahab, M., and Woodcock, J. P., Neural network analysis of doppler ultrasound blood flow signals: A pilot study. Ultrasound Med. Biol. 23(5):683-690, 1997.
5. Chan, B. C. B., Chan, F. H. Y., Lam, F. K., Lui, P. W., and Poon, P. W. F., Fast detection of venous air embolism is Doppler heart sound using the wavelet transform. IEEE Trans. Biomed. Eng. 44(4):237-245, 1997.
6. Das, R., and Sengur, A., Evaluation of ensemble methods for diagnosing of valvular heart disease. Expert Syst. Appl. 37(7):5110-5115, 2010.
7. Turkoglu, I., Arslan, A., and Ilkay, E., An expert system for diagnosis of the heart valve diseases. Expert Syst. Appl. 23:229-236, 2002.
8. Turkoglu, I., Arslan, A., and Ilkay, E., An intelligent system for diagnosis of heart valve diseases with wavelet packet neural networks. Comput. Biol. Med. 33(4):319-331, 2003.
9. Çomak, E., Arslan, A., and Turkoglu, I., A decision support system based on support vector machines for diagnosis of the heart valve diseases. Comput. Biol. Med. 37:21-27, 2007.
10. Uguz, H., Arslan, A., and Turkoglu, I. A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases, Pattern Recognition Letters, Available online 11 October 2006, 2007.
11. Sengur, A., An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases. Comput. Biol. Med. 38(3):329-338, 2008.
12. Sengur, A., An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Syst. Appl. 35(1, 2):214-222, 2008.
13. Sengur, A. and Turkoglu, I., A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases. Expert Syst. Appl. 35(3):1011-1020, 2008.
14. Devasahayam, S. R. (2000). Signals and systems in biomedical engineering. Kluwer Academic Publishers.
15. Das, R., Turkoglu, I and Sengur, A., Diagnosis of valvular heart disease through neural networks ensembles. Comput. Meth. Programs Biomed. 93(2):185-191, 2009.
16. Quiroga, R. Q., Roso, O. A., and Basar, E., Wavelet entropy: a measure of order in evoked potentials. Elsevier Sci. Evoked Potentials Magn. Fields September 49:298-302, 1999.
17. Vapnik, V., Statistical learning theory. Wiley, New York, 1998.
18. Siuly, Yan Li, Peng (Paul) Wen. Clustering technique-based least square support vector machine for EEG signal classification, Computer Methods and Programs in Biomedicine, In Press, Corrected Proof, Available online 17 December 2010.
19. Skurichina, M., and Duin, R. P. W., Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5(2):121-135, 2002.
20. Breiman, L., Bagging predictors. Mach. Learn. 24(2):123-140, 1996.
21. Freund, Y., and Shapire, R. E., A decision-Theoretic generalization of on line learning and an application to boosting. J. Comput. Syst. Sci. 55(1):119-139, 1997.
22. Ho, T. K., The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8):832-844, 1998.
23. http://www.bmj.com/cgi/reprint/308/6943/1552, Last accessed: 01.05.2008.
24. http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion-matrix/ confusion-matrix.html, Last accessed: 20.04.2008.
25. Sengur, A., Multiclass least-squares support vector machines for analog modulation classification. Expert Syst Appl 36(3):6681-6685, 2009.
26. Sun, S., Zhang, C., and Zhan, D., An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognit. Lett. 28(2007):2157-2163, 2007.