Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines

Maria Angélica de Oliveira Camargo-Brunetto, André R. Gonçalves

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is characterized by airflow limitation and the spirometry is one of the tests that can be used to detect such disease. However there is a great problem related to the different ways of interpreting the values provided by spirometric devices, regarding different guidelines and reference values. Artificial Neural Networks (ANN) can be used to help with tasks of diagnosis as that. This work presents the modeling and analysis of three ANN models to classify subjects with COPD, based on different sets of variables: a set of observed measures from spirometry and a set of interpreted values according to the guideline proposed by the American Thoracic Society. The results shown that it is possible to achieve a good accuracy in the diagnosis of COPD using ANNs, besides these features set conducted the COPD identification problem to a nearly linearly separable classification problem.

References

  1. Azeredo, C. A. C. (2002). Fisioterapia Respiratória Moderna. Manole, Sa˜o Paulo, 4 edition.
  2. Er, O. and Temurtas, F. (2008). A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks. Journal of Medical Systems, 32:429-432.
  3. Er, O., Yumusak, N., and Temurtas, F. (2010). Chest diseases diagnosis using artificial neural networks. Expert Systems with Applications, 37(12):7648-7655.
  4. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recogn. Lett., 27(8):861-874.
  5. Fontenla-Romero, O., Guijarro-Berdinas, B., AlonsoBetanzos, A., and Moret-Bonillo, V. (2005). A new method for sleep apnea classification using wavelets and feedforward neural networks. Artificial Intelligence in Medicine, 34:65-76.
  6. Group, A. W. (2004). Update to the Latin American Thoracic Society (ALAT) Recommendations on Infectious Exacerbation of COPD. Archivos de Bronconeumologia, 40:315-325.
  7. Lenfant, C. (1998). Estratégia global para o diagnóstico, a conduta e a prevenc¸ a˜o da doenc¸a pulmonar obstrutiva croˆnica. Technical report, GOLD - Global Initiative for Chronic Obstructive Lung Disease.
  8. Mehrabi, S., Maghsoudloo, M., Arabalibeik, H., Noormand, R., and Nozari, Y. (2009). Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases. Expert Systems with Applications, 36(3, Part 2):6956- 6959.
  9. Møller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4):525-533.
  10. Nathell, L., Nathell, M., Malmberg, P., and Larsson, K. (2007). COPD diagnosis related to different guidelines and spirometry techniques. Respiratory Research, 8.
  11. Passold, F., Garcia Ojeda, R., and Muniz Barreto, J. (1996). Hybrid expert system in anesthesiology for critical patients. In Electrotechnical Conference, 1996. MELECON 7896., 8th Mediterranean, volume 3, pages 1486- 1489.
  12. Wadie, B. S., Badawi, A. M., Abdelwahed, M., and Elemabay, S. M. (2006). Application of artificial neural network in prediction of bladder outlet obstruction: A model based on objective, noninvasive parameters. Urology, 68:1211-1214.
  13. Widrow, B. and Hoff, M. E. (1960). Adaptive switching circuits. In IRE WESCON Convention Record, Part 4, pages 96-104, New York. IRE.
  14. Yan, H., Jiang, Y., Zheng, J., Peng, C., and Li, Q. (2006). A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications, 30(2):272-281.
  15. Zhou, Z.-H. and Jiang, Y. (2003). Medical diagnosis with c4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42, 7(1):37-42.
Download


Paper Citation


in Harvard Style

de Oliveira Camargo-Brunetto M. and R. Gonçalves A. (2013). Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013) ISBN 978-989-8565-37-2, pages 207-214. DOI: 10.5220/0004234102070214


in Bibtex Style

@conference{healthinf13,
author={Maria Angélica de Oliveira Camargo-Brunetto and André R. Gonçalves},
title={Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)},
year={2013},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004234102070214},
isbn={978-989-8565-37-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)
TI - Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines
SN - 978-989-8565-37-2
AU - de Oliveira Camargo-Brunetto M.
AU - R. Gonçalves A.
PY - 2013
SP - 207
EP - 214
DO - 10.5220/0004234102070214