Akademik Veri Yönetim Sistemi
SENSORS, cilt.25, sa.17, 2025 (SCI-Expanded)
Highlights What are the main findings? A hybrid electronic nose system integrating 8 MOS and 14 QCM sensors effectively distinguished between lung cancer patients and healthy individuals through breath analysis. The fuzzy logic classifier optimized by a nature-inspired algorithm outperformed traditional methods, achieving 97.93% accuracy. What are the implications of the main findings? Demonstrates the strong potential of noninvasive electronic nose technology in early lung cancer diagnosis. Offers a reliable alternative to conventional diagnostic tools by combining intelligent algorithms with multidimensional sensor data.Highlights What are the main findings? A hybrid electronic nose system integrating 8 MOS and 14 QCM sensors effectively distinguished between lung cancer patients and healthy individuals through breath analysis. The fuzzy logic classifier optimized by a nature-inspired algorithm outperformed traditional methods, achieving 97.93% accuracy. What are the implications of the main findings? Demonstrates the strong potential of noninvasive electronic nose technology in early lung cancer diagnosis. Offers a reliable alternative to conventional diagnostic tools by combining intelligent algorithms with multidimensional sensor data.Abstract 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.