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

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.