In Journal of breath research
This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds (VOC) present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system is checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors (k-NN) gave an acceptable accuracy, sensitivity, and specificity of 91.3 %, 84.4 %, and 94.4 % respectively. Support vector machine (SVM) gave better results for COPD discrimination from controls with 90.9 % accuracy, 81.6 % sensitivity, and 95.8 % specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.
A Binson V, Subramoniam M, Mathew Luke
COPD, Electronic Nose, Lung Cancer, Machine learning