Welcome to the InfoSci Platform
Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air

Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air

Ludmila Ilieva Kuncheva, Juan Jose Rodríguez, Yasir Iftikhar Syed, Christopher O. Phillips, Keir Edward Lewis
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 15
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466613225|DOI: 10.4018/jkdb.2012040101
Cite Article Cite Article

MLA

Kuncheva, Ludmila Ilieva, et al. "Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air." IJKDB vol.3, no.2 2012: pp.1-15. http://doi.org/10.4018/jkdb.2012040101

APA

Kuncheva, L. I., Rodríguez, J. J., Syed, Y. I., Phillips, C. O., & Lewis, K. E. (2012). Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 3(2), 1-15. http://doi.org/10.4018/jkdb.2012040101

Chicago

Kuncheva, Ludmila Ilieva, et al. "Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 3, no.2: 1-15. http://doi.org/10.4018/jkdb.2012040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The diagnosis of Chronic Obstructive Pulmonary Disease (COPD) is based on symptoms, clinical examination, exposure to risk factors (smoking and certain occupational dusts) and confirming lung airflow obstruction (on spirometry). However, most people with COPD remain undiagnosed and controversies regarding spirometry persist. Developing accurate and reliable automated tests for the early diagnosis of COPD would aid successful management. We evaluated the diagnostic potential of a non-invasive test of chemical analysis (volatile organic compounds - VOCs) from exhaled breath. We applied 26 individual classifier methods and 30 state-of-the-art classifier ensemble methods to a large VOC data set from 109 patients with COPD and 63 healthy controls of similar age; we evaluated the classification error, the F measure and the area under the ROC curve (AUC). The results show that classifying the VOCs leads to substantial gain over chance but of varying accuracy. We found that Rotation Forest ensemble (AUC 0.825) had the highest accuracy for COPD classification from exhaled VOCs.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.