Effect of discretization method on the diagnosis of Parkinson's disease

International Journal of Innovative Computing, Information and Control

Volumn 7, Issue 8, 2011, Pages 4669-4678

  Kaya, Ersin ^a   Findik, Oǧuz ^a   Babaoǧlu, Ismail ^a   Arslan, Ahmet ^a  

^a Selçuk and cediluk University   (Turkey)

Author keywords

Classication methods; Entropy based discretization method; Parkinson's disease

Indexed keywords

CLASSICATION METHODS; CLASSIFICATION METHODS; DATA SETS; DISCRETIZATION METHOD; DISCRETIZATIONS; K-NEAREST NEIGHBORS; PARKINSON'S DISEASE; PRE-PROCESSING METHOD;

DISCRETE EVENT SIMULATION; ENTROPY; NEURODEGENERATIVE DISEASES; VOLUME MEASUREMENT;

DIAGNOSIS;

EID: 80052461768     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. A. E. Lang and A. M. Lozano, Parkinson's disease - First of two parts, The New England Journal of Medicine, vol.339, pp.1044-1053, 1998.
2. N. Singh, V. Pillay and Y. E. Choonara, Advances in the treatment of Parkinson's disease, Progr. Neurobiol, vol.81, pp.29-44, 2007.
3. National Collaborating Centre for Chronic Conditions, Parkinson's disease: National clinical guideline for diagnosis and management in primary and secondary care, Royal College of Physicians, 2006.
4. A. K. Ho, R. Iansek, C. Marigliani, J. L. Bradshaw and S. Gates, Speech impairment in a large sample of patients with Parkinson's disease, Behavioural Neurology, vol.11, pp.131-137, 1998.
5. J. A. Logemann, H. B. Fisher, B. Boshes and E. R. Blonsky, Frequency and co-occurrence of vocaltract dysfunctions in speech of a large sample of parkinson patients, Journal of Speech and Hearing Disorders, vol.43, pp.47-57, 1978.
6. M. A. Little, P. E. McSharry, S. J. Roberts, D. A. Costello and I. M. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection, Biomedical Engineering Online, vol.6, pp.23-58, 2007.
7. M. A. Little, E. McSharry, E. J. Hunter, J. Spielman and L. O. Ramig, Suitability of dysphonia measurements for telemonitoring of Parkinson's disease, IEEE Transactions on Biomedical Engineering, vol.56, pp.1015-1022, 2009.
8. A. Kumar and D. Zhang, Hand-geometry recognition using entropy-based discretization, IEEE Transactions on Information Forensics and Security, vol.2, pp.181-187, 2007.
9. K. Polat, S. Kara, A. Güven and S. Güneş, Utilization of discretization method on the diagnosis of optic nerve disease, Computer Methods and Programs in Biomedicine, vol.91, pp.255-264, 2008.
10. R. Abraham, J. Simha and S. Iyengar, A comparative analysis of discretization methods for medical datamining with Naïve Bayesian clasifier, The 9th International Conference on Information Technology, pp.235-236, 2006.
11. J. Demsar, B. Zupan, N. Aoki, M. J. Wall, T. H. Granchi and J. R. Beck, Feature mining and predictive model construction from severe trauma patient's data, International Journal of Medical Informatics, vol.63, pp.41-50, 2001.
12. S. Acid, L. M. Campos, J. M. Fernandez-Luna, S. Rodriguez, J. M. Rodriguez and J. L. Salcedo, A comparison of learning algorithms for Bayesian networks: A case study based on data from an emergency medical service, Artificial Intelligence in Medicine, vol.30, pp.215-232, 2004.
13. M. K. Ismail and V. Ciesielski, An empirical investigation of the impact of discretization on common data distributions, Design and Application of Hybrid Intelligent Systems, pp.692-701, 2003.
14. H. Kodaz, S. Özşen, A. Arslan and S. Güneş, Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease, Expert Systems with Applications, vol.36, no.2, pp.3086-3092, 2009.
15. U. M. Fayyad and K. B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, The 13th International Joint Conference on Artificial Intelligence, pp.1022-1027, 1993.
16. H. Kima and S. Chen, Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents, Pattern Recognition, vol.42, pp.1777-1785, 2009.
17. C. Hsu, H. Huang and T. Wong, On why discretization works for Naïve Bayesian, Lecture Notes in Computer Science, pp.440-452, 2000.
18. M. Hill and M. T. Mitchell, Machine Learning, Singapore, 1997.
19. J. R. Quinlan, Induction of C4.5 decision trees, Machine Learning, vol.1, pp.81-106, 1986.
20. G. Shakhnarovish, T. Darrell and P. Indyk, Nearest-Neighbor Methods in Learning and Vision, MIT Press, 2005.
21. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
22. K. Y. Chen and C. H. Wang, A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Systems with Applications, vol.32, pp.254-264, 2007.
23. E. Çomak, A. Arslan and İ. Türkoq̌lu, A decision support system based on support vector machines for diagnosis of the heart valve diseases, Computers in Biology and Medicine, vol.37, pp.21-27, 2007.
24. K. Takeuchi and N. Collier, Bio-medical entity extraction using support vector machines, Artificial Intelligence in Medicine, vol.33, no.2, pp.125-137, 2003