The lungs are the most important organ of the respiratory system. The volatile organic compounds found in breath, which usually originate from the blood and enable us to observe different processes in the body, are carried to the lungs through the blood and then exhaled by breath. In this study, a mixed sensor based electronic nose circuit was developed using eight metal oxide semiconductors and 14 Quartz Crystal Microbalance gas sensors. A total of 100 volunteers participated in the study, including 20 healthy volunteers who did not smoke, 20 healthy volunteers who smoked, and 60 lung cancer_volunteers. Throughout this study, 338 experiments were conducted using breath samples. Data dimension reduction was achieved using linear discriminant analysis and principal component analysis algorithms. The individual classification accuracies for the metal oxide semiconductor and quartz crystal microbalance sensor data are 81.54% and 73.18%, respectively. Upon combining the sensor data, a noticeable increase in accuracy of 85.26% was observed. In this study, the performance of the developed system was enhanced using principal component and linear discriminant analyses. While the highest classification accuracy increased to 88.56% with the feature matrix obtained using the principal component analysis method, this value was obtained with 94.58% accuracy using the linear discriminant analysis method.

In this study, a hybrid sensor-based electronic nose circuit was developed using eight metal-oxide semiconductors and 14 quartz crystal microbalance gas sensors. This study included 100 participants: 60 individuals diagnosed with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers. A total of 338 experiments were performed using breath samples throughout this study. In the classification phase of the obtained data, in addition to traditional classification algorithms, such as decision trees, support vector machines, k-nearest neighbors, and random forests, the fuzzy logic method supported by the optimization algorithm was also used. While the data were classified using the fuzzy logic method, the parameters of the membership functions were optimized using a nature-inspired optimization algorithm. In addition, principal component analysis and linear discriminant analysis were used to determine the effects of dimension-reduction algorithms. As a result of all the operations performed, the highest classification accuracy of 94.58% was achieved using traditional classification algorithms, whereas the data were classified with 97.93% accuracy using the fuzzy logic method optimized with optimization algorithms inspired by nature.

In this study, a hybrid sensor-based electronic nose circuit was developed using eight metal-oxide semiconductors and 14 quartz crystal microbalance gas sensors. The study included 100 participants: 60 individuals diagnosed with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers. A total of 338 experiments were performed using the breath samples throughout the study. In the classification phase of the obtained data, in addition to traditional classification algorithms, such as decision trees, support vector machines, k-nearest neighbors, and random forests, the fuzzy logic method supported by the optimization algorithm was also used. While the data were classified using the fuzzy logic method, the parameters of the membership functions were optimized using a nature-inspired optimization algorithm. In addition, principal component analysis and linear discriminant analysis were used to determine the effects of dimension-reduction algorithms. As a result of all the operations performed, the highest classification accuracy of 94.58% was achieved using traditional classification algorithms, whereas the data were classified with 97.93% accuracy using the fuzzy logic method optimized with optimization algorithms inspired by nature.

Pamukkale University Journal of Engineering Sciences