Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods

• Published in: Clinica chimica acta Vol. 523; pp. 231 - 238
• Main Authors: V.A., Binson, Subramoniam, M., Mathew, Luke
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2021
• Subjects: Breath analysis; Breath Tests; COPD; Electronic Nose; Ensemble learning; Humans; KPCA; Lung cancer; Lung Neoplasms - diagnosis; Machine Learning; Pulmonary Disease, Chronic Obstructive - diagnosis; Volatile Organic Compounds; KPCA; Ensemble learning; Lung cancer; Electronic nose; Breath analysis; COPD
• DOI: 10.1016/j.cca.2021.10.005
• ISSN: 0009-8981; 1873-3492

•An e-nose system was developed with five MOS gas sensors.•Non-invasive diagnosis of COPD and lung cancer was done through breath analysis.•Selecting patients and controls of almost same age group is a challenging task.•Proper selection of sensors and classifiers can give better classification results.•Ensemble learning method XGBoost had given better results in the discrimination. The chemical gas sensor array based electronic-nose (e-nose) devices with machine learning algorithms can detect and differentiate expelled breath samples of patients with various respiratory ailments and controls. It is by the recognition of levels and variations of volatile organic compounds (VOC) in the exhaled air. Here, we aimed to differentiate chronic obstructive pulmonary disease (COPD) and lung cancer from controls. This work presents the details of the developed e-nose system, selection of the study subjects, exhaled breath sampling method and detection, and the data analysis algorithms. The developed device is tested in 199 participants including 93 controls, 55 COPD patients, and 51 lung cancer patients. The main advantage of the device is robustness and portability and cost-effectiveness. In the training phase and model validation phase, the ensemble learning method XGBoost outperformed the other two models. In the prediction of lung cancer, XGBoost method attained a classification accuracy of 79.31%. In COPD prediction also the same method had given the better results with 76.67% accuracy. The e-nose system developed with TGS gas sensors was portable, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different and hence the e-nose system can be used as a potential diagnostic device for patients with lung diseases.