Skip to main content
Have a personal or library account? Click to login
Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression Cover

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

Open Access
|May 2010

References

  1. Miller, M.R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C.P.M., Gustafsson, P., Jensen, R., Johnson, D.C., MacIntyre, N., McKay R., Navajas, D., Pedersen, O.F., Pellegrino, R., Viegi, G. and Wanger J. (2005)., 26, 319-338.
  2. Pierce, R. (2004). Spirometer: An essential clinical measurement., 34, 535-539.
  3. American Thoracic Society. (1991). Lung function testing: selection of reference values and interpretative strategies., 144: 1202-18.
  4. Wagner, N. L., Beckett, W. S. and Steinberg, R. (2006). Using Spirometry results in occupational medicine and research: common errors and good practice in statistical analysis and reporting,, 10, 5-10.
  5. David, P. J., Pierce, R. (2008). Spirometry-The measurement and interpretation of ventilatory function in clinical practice., 3rd edition. 1-24.
  6. Aaron, S. D., Dales, R. E. and Cardinal. P. (1999). How accurate is spirometry at predicting restrictive pulmonary impairment., 115, 869-873.
  7. Sahin, D., Ubeyli, E.D., Ilbay, G., Sahin, M. and Yasar, A. B. (2009). Diagnosis of airway obsruction or restrictive spirometric patterns by multiclass support vector machines,, DOI 10.1007/s10916-009-9312-7.
  8. Ulmer, W.T. (2003). Lung function - Clinical importance, problems and new results., 54, 11-13.
  9. Schermer, T.R., Jacobs, J.E. and Chavennes, N.H. (2003). Validity of spirometric testing in a general practice population of patients with chronic obstructive pulmonary disease (COPD),, 58, 861-866.
  10. Sujatha C. M. and Ramakrishnan S. (2009). Prediction of Forced Expiratory Volume in Normal and restrictive respiratory functions using spirometry and self organizing map,, 33, 19-32.
  11. Sujatha C. M., Mahesh V. and Ramakrishnan S. (2008). Comparison of two ANN methods for classification of spirometer data,, 8 (2), 53-57.
  12. Smola A. J. and Schölkopf B. (2004). A tutorial on support vector regression,, 14, 199-222.
  13. Vapnik V. N. (1998). Statistical Learning Theory. New York: John Wiley & Sons.
  14. Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press.
  15. Lee, J., Blain, S., Casas, M., Kenny, D., and Berall, G. (2006). A radial basis classifier for the automatic detection of aspiration in children with dysphagia,, 3, 1-17.
  16. Tung-Kuang W., Shian-Chang H. and Ying-Ru M. (2008). Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities,, 34, 1846-1856.
  17. Chen K., Kurgan M. and Kurgan M. (2008). Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values,, 1, 1-9.
  18. Schölkopf B., Kah-Kay S., Christopher J. C. B., Federico G., Partha N., Tomaso P., and Vapnik V. (1997). Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers,, 45 (11), 2758-2765.
  19. Cooper, B.G. and Madsen, F. (2000).3, 40-43.
  20. Hua, S. and Sun Z. (2001). A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach., 308, 397-407.
  21. Pai, P. F., Lin, C. H., Hong, W. C. and Chen, C. T. (2006). A Hybrid support vector machine regression for exchange rate prediction,, 17, 19-32.
Language: English
Page range: 63 - 67
Published on: May 14, 2010
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2010 A. Kavitha, C. Sujatha, S. Ramakrishnan, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons License.