Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air
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Summary
The results show that classifying the VOCs leads to substantial gain over chance but of varying accuracy, and Rotation Forest ensemble AUC 0.825 had the highest accuracy for COPD classification from exhaled V OCs.
- Type
- article
- Published
- 2012-04-01
- Cited by
- 9
- References
- 44
- OpenAlex
- https://openalex.org/W1964091492
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44870979
Keywords
COPD, Spirometry, Medicine, Exhaled air, Pulmonary disease
References
- Pattern Classification
- Pattern Classification Using Ensemble Methods
- Pattern Recognition and Machine Learning
- The application of statistical methods using VOCs to identify patients with lung cancer
- A decision-theoretic generalization of on-line learning and an application to boosting
- Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma.
- Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography.
- Development of accurate classification method based on the analysis of volatile organic compounds from human exhaled air.
- Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls
- Improved Boosting Algorithms Using Confidence-rated Predictions
- Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines
- Classification and regression trees
- Detection of lung cancer with volatile markers in the breath.
- The Elements of Statistical Learning
- Volatile biomarkers of pulmonary tuberculosis in the breath.
- A profile of volatile organic compounds in breath discriminates COPD patients from controls.
- MultiBoosting: A Technique for Combining Boosting and Wagging
- The Random Subspace Method for Constructing Decision Forests
- Small-sample precision of ROC-related estimates
- Detection of lung cancer by sensor array analyses of exhaled breath.
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- A Hybrid Ensemble Feature Selection-Based Learning Model for COPD Prediction on High-Dimensional Feature Space
- JFeature: A Java Package for Extracting Global Sequence Features from Proteins for Functional Classification
- COPD_A_175706 1465..1484
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