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


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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). Standardisation of spirometry. European Respiratory Journal, 26, 319-338.10.1183/09031936.05.0003480516055882Search in Google Scholar

Pierce, R. (2004). Spirometer: An essential clinical measurement. Australian Family Phyician, 34, 535-539.Search in Google Scholar

American Thoracic Society. (1991). Lung function testing: selection of reference values and interpretative strategies. American Reviews on Respiratory Diseases, 144: 1202-18.Search in Google Scholar

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, Indian Journal of Occupational Environmental Medicine, 10, 5-10.10.4103/0019-5278.22888Search in Google Scholar

David, P. J., Pierce, R. (2008). Spirometry-The measurement and interpretation of ventilatory function in clinical practice. Spirometry Handbook, 3rd edition. 1-24.Search in Google Scholar

Aaron, S. D., Dales, R. E. and Cardinal. P. (1999). How accurate is spirometry at predicting restrictive pulmonary impairment. Chest, 115, 869-873.10.1378/chest.115.3.86910084506Search in Google Scholar

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, Journal of Medical Systems, DOI 10.1007/s10916-009-9312-7.Search in Google Scholar

Ulmer, W.T. (2003). Lung function - Clinical importance, problems and new results. Journal of Physiology and Pharmacology, 54, 11-13.Search in Google Scholar

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), Thorax, 58, 861-866.10.1136/thorax.58.10.861174649714514938Search in Google Scholar

Sujatha C. M. and Ramakrishnan S. (2009). Prediction of Forced Expiratory Volume in Normal and restrictive respiratory functions using spirometry and self organizing map, Journal of Medical Engineering and Technology, 33, 19-32.10.1080/0309190090296071019484651Search in Google Scholar

Sujatha C. M., Mahesh V. and Ramakrishnan S. (2008). Comparison of two ANN methods for classification of spirometer data, Measurement Science Review, 8 (2), 53-57.10.2478/v10048-008-0014-ySearch in Google Scholar

Smola A. J. and Schölkopf B. (2004). A tutorial on support vector regression, Statistical Computing, 14, 199-222.10.1023/B:STCO.0000035301.49549.88Search in Google Scholar

Vapnik V. N. (1998). Statistical Learning Theory. New York: John Wiley & Sons.Search in Google Scholar

Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press.Search in Google Scholar

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, Journal of Neuroengineering and Rehabilitation, 3, 1-17.10.1186/1743-0003-3-14157035716846507Search in Google Scholar

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, Expert Systems Applications, 34, 1846-1856.10.1016/j.eswa.2007.02.026Search in Google Scholar

Chen K., Kurgan M. and Kurgan M. (2008). Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values, Journal Biomedical Science Engineering, 1, 1-9.10.4236/jbise.2008.11001Search in Google Scholar

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, IEEE Transactions on Signal Processing, 45 (11), 2758-2765.10.1109/78.650102Search in Google Scholar

Cooper, B.G. and Madsen, F. (2000). European Respiratory buyers guide. 3, 40-43.Search in Google Scholar

Hua, S. and Sun Z. (2001). A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach. Journal of Molecular Biology, 308, 397-407.10.1006/jmbi.2001.458011327775Search in Google Scholar

Pai, P. F., Lin, C. H., Hong, W. C. and Chen, C. T. (2006). A Hybrid support vector machine regression for exchange rate prediction, Information and Management Sciences, 17, 19-32.Search in Google Scholar

eISSN:
1335-8871
Język:
Angielski
Częstotliwość wydawania:
6 razy w roku
Dziedziny czasopisma:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing