Abstract:

Lung cancer has the highest death rates among all types of cancer worldwide. Detection of lung cancer in its early stages significantly increases the survival rate. In this study, the aim is to improve the lung cancer detection performance of electronic noses (e-noses) with breath analysis by using two different types of gas sensor-based e-nose. The developed e-nose system consists of 14 quartz crystal microbalance (QCM) sensors and 8 metal oxide semiconductor (MOS) sensors. Breath samples were collected from a total of 100 volunteers, including 60 patients with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers, and were classified using decision tree (DT), support vector machine (SVM), k-nearest neighbour (kNN), and random forest (RF) algorithms. Principal component analysis (PCA) and linear discriminant analysis (LDA) algorithms were used for dimension reduction. A classification accuracy of 86.34% and 75.48% was obtained using MOS and QCM sensor data, respectively. The overall results have shown that combining the sensor data increases the accuracy to 88.54%. Additionally, it can be indicated that the PCA and LDA algorithms have a positive effect on the performance. By using PCA and LDA algorithms, the accuracy increased up to 92.67% and 95.36%, respectively.

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