One-class classification for heart disease diagnosis

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn 2014-January, Issue January, 2014, Pages 2551-2556

  Cabral, George Gomes ^a   De Oliveira, Adriano Lorena Ińacio ^b  

^a UNIVERSIDADE FEDERAL RURAL DE PERNAMBUCO   (Brazil)

^b FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CARDIOLOGY; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CYBERNETICS; DISEASES; HEART; HOSPITALS; LEARNING SYSTEMS; PATIENT TREATMENT;

HEART DISEASE; HEART DISEASE DIAGNOSIS; LARGE AMOUNTS; MACHINE LEARNING TECHNIQUES; MEDICAL CLINICS; ONE-CLASS CLASSIFICATION; ONE-CLASS CLASSIFIER; SPECIFIC PROBLEMS;

DIAGNOSIS;

EID: 84938149392     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/smc.2014.6974311     Document Type: Conference Paper

References (29)

1. B. Scholkopf, J. C. Platt, J. S. Taylor, A. J. Smola and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation. Vol. 13(7), pp. 1443-1471, 2001.
2. D. M. J. Tax.One-class classification concept-learning in the absence of counter-examples. Ph.D. thesis, Technische Universiteit Delft, 2001.
3. H. Hoffmann. Kernel pca for novelty detection. Pattern Recognition. Vol. 40, pp. 863-874, 2007.
4. J. Kennedy. Why does it need velocity? In: Swarm Intelligence Symposium. pp. 38-44, 2005.
5. J. Kennedy and R. Eberhart. Particle swarm optimization. In: IEEE International Conference on Neural Networks (ICNN'95). Vol. 4, pp. 1942-1947, 1995
6. A. P. Engelbrecht. Fundamentals of Computational Swarm Intelligence. Wiley & Sons, 2005.
7. D. Bratton and J. Kennedy. Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium. pp. 120-127, 2007.
8. J. S. Sartakhti, M. H. Zangooei and K. Mozafari. Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA), Computer Methods and Programs in Biomedicine. Vol. 108(2), pp. 570-579, 2012.
9. O. Er, N. Yumusak and F. Temurtas. Chest diseases diagnosis using artificial neural networks. Expert Systems with Applications. Vol. 37(12), pp. 7648-7655, 2010.
10. H. Temurtas, N. Yumusak and F. Temurtas. A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with Applications, Vol. 36, pp. 8610-8615, 2009.
11. W. Qian, T. Zhukov, D. S. Song and M.S. Tockman. Computerized analysis of cellular features and biomarkers for cytologic diagnosis of early lung cancer. Analytical and Quantitative Cytology and Histology. Vol. 29(2), pp. 103-111, 2007.
12. J. Jiang, P. Trundle and J. Ren. Medical image analysis with artificial neural networks. Computerized Medical Imaging and Graphics. Vol. 34(2) pp. 617-631, 2010.
13. J. A. L. Vallverdu, D. Riano and J. A. Bohada. Improving medical decision trees by combining relevant health-care criteria. Expert Systems with Applications. Vol. 39(14), pp. 11782-11791, 2012.
14. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
15. R. M. Rahman and F. R. Md. Hasan. Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data, Expert Systems with Applications, Vol. 38(9), pp. 11421-11436, 2011.
16. W. Horn. AI in medicine on its way from knowledge-intensive to dataintensive systems. Artificial intelligence in medicine. Vol. 23(1), pp. 5-12, 2001.
17. World Health Organization. The world health report 2010 - Health systems financing: The path to universal coverage, 2010.
18. S. Ainsworth, J. P. Wyllie and C. Wren. Prevalence and clinical significance of cardiac murmurs in neonates. Archives of disease in childhood. Fetal and neonatal edition. Vol. 80(1), pp. 43-45, 1999.
19. A. Rein and S. Omokhodion. Significance of a cardiac murmur as the sole clinical sign in the newborn. CLIN PEDIATR. n. 39, pp. 511-520, 2000.
20. R. Gupta, A. Mittal and K. Singh. A Novel and Efficient Technique for Identification and Classification of GPCRs. IEEE Transactions on Information Technology in Biomedicine. Vol. 12(4), pp. 541-548, 2008.
21. P. Baldi, S. Brunak, Y. Chauvin, C. A. F. Andersen and H. Nielsen. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics, 16(5), pp. 412-424, 2000.
22. G. G. Cabral and A. L. I. Oliveira. One-class classification through optimized feature boundaries detection and prototype reduction. International Conference on Artificial Neural Networks. pp. 693-700, 2012.
23. M. Farquad and I. Bose. Preprocessing unbalanced data using support vector machine. Decision Support Systems, Vol. 53(1), pp. 226-233, 2012.
24. L. Cao, H. P. Lee and W. K. Chong. Modified support vector novelty detector using training data with outliers. Pattern Recognition Letters. Vol. 24 (14), 2479-2487, 2003.
25. K. I. Diamantaras and S. Y. Kung. Principal Component Neural Networks. Wiley, New York, 1996.
26. M. Kemmler, E. Rodner and J. Denzler. One-class classification with Gaussian processes. In 10th Asian Conference on Computer Vision, Vol. 2, pp. 1025-1036, 2010.
27. A. Frank and A. Asuncion. UCI machine learning repository, 2010 (available at http://archive.ics.uci.edu/ml).
28. J. Demsar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research. Vol. 7, pp. 1-30, 2006.
29. M. R. Wu and J. P. Ye. A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans. Pattern Analysis and Machine Intelligence. Vol. 31(11), pp. 2088-2092, 2009.