Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

Computer Methods and Programs in Biomedicine

Volumn 105, Issue 3, 2012, Pages 183-193

  Amaral, Jorge L M ^a   Lopes, Agnaldo J ^b   Jansen, José M ^b   Faria, Alvaro C D ^a   Melo, Pedro L ^a  

^a STATE UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b HOSPITAL UNIVERSITÁRIO PEDRO ERNESTO   (Brazil)

Author keywords

Artificial intelligence; Chronic obstructive pulmonary disease; Classification; Clinical decision support; Forced oscillation technique; Respiratory system

Indexed keywords

AREA UNDER THE ROC CURVE; ARTIFICIAL NEURAL NETWORK; AUTOMATIC IDENTIFICATION; CHRONIC OBSTRUCTIVE PULMONARY DISEASE; CLASSIFICATION ALGORITHM; CLINICAL DECISION SUPPORT; CLINICAL DECISION SUPPORT SYSTEMS; CLINICAL DIAGNOSIS; DATA SETS; FEATURE SELECTION METHODS; FORCED OSCILLATION TECHNIQUES; FORCED OSCILLATIONS; HIGH DEGREE OF ACCURACY; INPUT FEATURES; K-NEAREST NEIGHBORS; ON-MACHINES; REDUCED DATA;

ARTIFICIAL INTELLIGENCE; AUTOMATION; CLASSIFICATION (OF INFORMATION); DECISION SUPPORT SYSTEMS; DECISION TREES; DIAGNOSIS; FEATURE EXTRACTION; LEARNING ALGORITHMS; MEASUREMENTS; NEURAL NETWORKS; RESPIRATORY SYSTEM; SUPPORT VECTOR MACHINES;

PULMONARY DISEASES;

AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOANALYSIS; BAYES THEOREM; CHRONIC OBSTRUCTIVE LUNG DISEASE; DECISION SUPPORT SYSTEM; DECISION TREE; FORCED OSCILLATION; HUMAN; K NEAREST NEIGHBOR; LEARNING ALGORITHM; MACHINE LEARNING; OSCILLATION; PERFORMANCE MEASUREMENT SYSTEM; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; NEURAL NETWORKS (COMPUTER); PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; SUPPORT VECTOR MACHINES;

EID: 84856228303     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.09.009     Document Type: Article

References (48)

1. The global initiative for chronic obstructive lung disease. Available from: (accessed March 2011). http://www.goldcopd.com/.
2. World Health Organization. Available from: (accessed March 2011). http://www.who.int/respiratory/copd/burden/en/index.html.
3. Enright P.L., Crapo R.M. Controversies in the use of spirometry for early recognition and diagnosis of chronic obstructive pulmonary disease in cigarette smokers. Clin. Chest Med. 2000, 21(4):645-652.
4. Ljung L. System Identification: Theory for the User 1987, Prentice-Hall Inc., Londres.
5. Dubois A.B., Brody A.W., Lewis D.H., Burges B.F. Oscillation mechanics of lungs and chest in man. J. Appl. Physiol. 1956, 8:587-594.
6. Faria A.C.D., Lopes A.J., Jansen J.M., Melo P.L. Evaluating the forced oscillation technique in the detection of early smoking-induced respiratory changes. Biomed. Eng. Online 2009, 25:8-22.
7. Orr A.J., Westenskow D.R. A breathing circuit alarm system based on neural networks. J. Clin. Monit. 1994, 10:101-109.
8. Bright P., Miller M.R., Franklyn J.A., Sheppard M.C. The use of a neural network to detect upper airway obstruction caused by goiter. Am. J. Respir. Crit. Care Med. 1998, 157:1885-1891.
9. Leon M.A., Räsänen J., Mangar D. Neural network-based detection of esophageal intubation. Anesth. Analg. 1994, 78:548-553.
10. Räsänen J., León M.A. Detection of lung injury with conventional and neural network-based analysis of continuous data. J. Clin. Monit. 1998, 14:433-439.
11. Perchiazzi G., Högman M., Rylander C., Giuliani R., Fiore T., Hedenstierna G. Assessment of respiratory system mechanics by artificial neural networks: an exploratory study. J. Appl. Physiol. 2001, 90:1817-1824.
12. Uncü U. Evaluation of pulmonary function tests by using fuzzy logic theory. J. Med. Syst. 2010, 34(3):241-250.
13. Lopes A.J., Capone D., Mogami R., Lanzillotti R.S., Melo P.L., Jansen J.M. Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic. Clinics 2011, 66(6):1015-1019.
14. Di Mango A.M.G.T., Lopes A.J., Jansen J.M., Melo P.L. Changes in respiratory mechanics with degrees of airway obstruction in COPD: detection by forced oscillation technique. Respir. Med. 2006, 100(3):399-410.
15. Ionescu C., Derom E., De Keyser R. Assessment of respiratory mechanical properties with constant-phase models in healthy and COPD lungs. Comput. Methods Programs Biomed. 2010, 97(1):78-85.
16. Barúa M., Nazeran H., Nava P., Granda V., Diong B. Classification of pulmonary diseases based on impulse oscillometric measurements of lung function using neural networks. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2004, 3848-3851.
17. Barúa M., Nazeran H., Nava P., Diong B., Goldman M. Classification of impulse oscillometric patterns of lung function in asthmatic children using artificial neural networks. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005, 327-331.
18. Macleod D., Birch M. Respiratory input impedance measurements: forced oscillation methods. Med. Biol. Eng. Comput. 2001, 39:505-516.
19. Hellinckx J., Cauberghs M., De Boeck K., Demedts M. Evaluation of impulse oscillation system: comparison with forced oscillation technique and body plethysmography. Eur. Respir. J. 2001, 18:564-570.
20. Amaral J.L.M., Faria A.C.D., Lopes A.J., Jansen J.M., Melo P.L. Automatic identification of chronic obstructive pulmonary disease based on forced oscillation measurements and artificial neural networks. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina 2010.
21. Kuncheva L.I. Combining Pattern Classifiers: Methods and Algorithms 2004, Wiley-Interscience.
22. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2000, Wiley-Interscience.
23. Tan P.N., Steinbach M., Kumar V. Introduction to Data Mining 2006, University of Minnesota Publisher, Addison-Wesley Copyright.
24. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann. 2nd ed.
25. Haykin S. Neural Networks a Comprehensive Foundation 1994, Macmillan College Publishing Company, Englewood Cliffs.
26. Vapnik V.N. The Nature of Statistical Learning Theory 2000, Springer, New York. 2nd ed.
27. Zhang G.P. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 2000, 30:451-462.
28. Pedreira C.E., Macrini L., Land M.G., Costa E.S. New decision support tool for treatment intensity choice in childhood acute lymphoblastic leukemia. IEEE Trans. Inf. Technol. Biomed. 2009, 13:284-290.
29. Goldbaum M.H., Sample P.A., Chan K., Williams J., Lee T.-W., Blumenthal E., Girkin C.A., Zangwill L.M., Bowd C., Sejnowski T., Weinreb R.N. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest. Ophthalmol. Vis. Sci. 2002, 43(1):162-169.
30. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
31. Dietterich D.T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:1895-1923.
32. Fawcett T. An introduction to roc analysis. Pattern Recogn. Lett. 2006, 27(8):861-874.
33. Refaeilzadeh P., Tang L., Liu H. Cross Validation, Encyclopedia of Database Systems 2009, Springer.
34. Demsar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7:1-30.
35. Cavalcanti J.V., Lopes A.J., Jansen J.M., Melo P.L. Detection of changes in respiratory mechanics due to increasing degrees of airway obstruction in asthma by the forced oscillation technique. Respir. Med. 2006, 100(12):2207-2219.
36. Melo P.L. New impedance spectrometer for scientific and clinical studies on the respiratory system. Rev. Sci. Instrum. 2000, 71(7):2867-2872.
37. Melo P.L., Werneck M.M., Giannella-Neto A. Influence of the pressure generator non-linearities in the accuracy of respiratory input impedance measured by forced oscillation. Med. Biol. Eng. Comput. 2000, 38:102-108.
38. Faria A.C.D., Lopes A.J., Jansen J.M., Melo P.L. Assessment of respiratory mechanics in patients with sarcoidosis using forced oscillation: correlations with spirometric and volumetric measurements and diagnostic accuracy. Respiration 2009, 78(1):93-104.
39. Duin R.P.W., Juszczak P., Paclik P., Pekalska E., de Ridder D., Tax D.M.J., Verzakov S. PRTools4.1, A Matlab Toolbox for Pattern Recognition 2007, Delft University of Technology.
40. C.W. Hsu, C.C. Chang, C.J. Lin, A practical guide to support vector classification. Available from: (accessed October 2010). http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
41. Martinez W.L., Martinez A.R. Exploratory Data Analysis with MATLAB 2005, CRC Press.
42. Conover W.J. Practical Nonparametric Statistics 1999, Wiley. 3rd ed.
43. Delany S.J., Cunningham P., Coyle L. An assessment of case-based reasoning for spam filtering. Artif. Intell. Rev. 2005, 24(3):359-378.
44. Hanley J.A., McNeil B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982, 143:29-36.
45. Swets J.A., Picket R.M. Evaluation of diagnostic systems: methods from signal detection theory. Med. Phys. 1983, 10(2):266-267.
46. Swets J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240:1285-1293.
47. Golpe R., Jiménez A., Carpizo R., Cifrian J.M. Utility of home oximetry as a screening test for patients with moderate and severe symptoms of obstructive sleep apnea. Sleep 1999, 22(7):932-937.
48. Croxton T.L., Weinmann G.G., Senior R.M., Hoidal J.R. Future research directions in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2002, 165:838-844.