Evaluation of ensemble methods for diagnosing of valvular heart disease

Expert Systems with Applications

Volumn 37, Issue 7, 2010, Pages 5110-5115

  Das, Resul ^a   Sengur, Abdulkadir ^a  

^a FIRAT UNIVERSITY   (Turkey)

Author keywords

Bagging; Boosting; Doppler heart sounds; Ensemble methods; Heart valves; Random subspaces

Indexed keywords

COMPARATIVE STUDIES; DATA SETS; DOPPLER HEART SOUNDS; ENSEMBLE CLASSIFICATION; ENSEMBLE LEARNING; ENSEMBLE METHODS; HEART DISEASE; HEART VALVES; MACHINE-LEARNING; RANDOM SUBSPACES;

CARDIOLOGY; DIAGNOSIS; LEARNING ALGORITHMS;

HEART;

EID: 77950188774     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.12.085     Document Type: Article

References (22)

1. Bishop C.M. Neural networks for pattern recognition (1995), Oxford University Press
2. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
3. 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 Transactions on Biomedical Engineering 44 4 (1997) 237-245
4. Coifman R.R., and Wickerhauser M.V. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory 38 2 (1992) 713-718
5. Comak E., Arslan A., and Turkoglu I. A decision support system based on support vector machines for diagnosis of the heart valve diseases. Computers in Biology and Medicine 37 (2007) 21-27
6. Daş R., Türkoǧlu I., and Şengür A. Diagnosis of valvular heart disease through neural networks ensembles. Computer Methods and Programs in Biomedicine 93 2 (2009) 185-191
7. Duda R.O., and Hart P.E. Pattern classification and scene analysis (1973), Wiley, New York
8. Freund Y., and Shapire R.E. A decision-theoretic generalization of on line learning and an application to boosting. Journal of Computer and System Sciences 55 1 (1997) 119-139
9. Guler I., Kiymik M.K., Kara S., and Yuksel M.E. Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time. International Journal of Bio-Medical Computing 31 3-4 (1992) 247-256
10. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 (1998) 832-844
11. Keeton P.I.J., and Schlindwein F.S. Application of wavelets in Doppler ultrasound. MCB University Press 17 1 (1997) 38-45
12. Quiroga R.Q., Roso O.A., and Basar E. Wavelet entropy: a measure of order in evoked potentials. Elsevier Science, Evoked Potentials and Magnetic Fields 49 September (1999) 298-302
13. Sengur A. An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases. Computers in Biology and Medicine 38 3 (2008) 329-338
14. Sengur A. An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Systems with Applications 35 1-2 (2008) 214-222
15. 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 Systems with Applications 35 3 (2008) 1011-1020
16. Skurichina M., and Duin R.P.W. Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications 5 2 (2002) 121-135
17. Sun S., Zhang C., and Zhan D. An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters 28 (2007) 2157-2163
18. Turkoglu I., Arslan A., and Ilkay E. An expert system for diagnosis of the heart valve diseases. Expert Systems with Applications 23 (2002) 229-236
19. Turkoglu I., Arslan A., and Ilkay E. An intelligent system for diagnosis of heart valve diseases with wavelet packet neural networks. Computer in Biology and Medicine 33 4 (2003) 319-331
20. Uguz, H., Arslan, A., & Turkoglu, I. (2007). A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases. Pattern Recognition Letters, Available online 11 October 2006.
21. Vapnik V. Statistical learning theory (1998), John Wiley and Sons, New York
22. 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 in Medicine and Biology 23 5 (1997) 683-690