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

Authors:
Ludmila Ilieva Kuncheva;Juan Jose Rodríguez;Yasir Iftikhar Syed;Christopher O. Phillips;Keir Edward Lewis
Affiliations:
School of Computer Science, Bangor University, Bangor Gwynedd, UK;Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain;Respiratory Department, Prince Philip Hospital, Dafen, Llanelli, UK;Welsh Centre for Printing and Coating, College of Engineering, Swansea University, Swansea, UK;Respiratory Department, Prince Philip Hospital, & College of Medicine, Swansea University, Llanelli, UK
Venue:
International Journal of Knowledge Discovery in Bioinformatics
Year:
2012

Citing 19
Cited 0

Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Random Forests

Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Small-sample precision of ROC-related estimates

Bioinformatics
Small-sample precision of ROC-related estimates

Bioinformatics
Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines

Journal of Medical Systems
Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods
Boosting: Foundations and Algorithms

Boosting: Foundations and Algorithms
Ensemble Methods: Foundations and Algorithms

Ensemble Methods: Foundations and Algorithms

Quantified Score

Hi-index	0.00

Visualization

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.