Development of an E-nose system using machine learning methods to predict ventilator-associated pneumonia

Currently, no device is available for the rapid screening of ventilator-associated pneumonia (VAP) at an early stage. Accordingly, we propose the design of an offline gas detection system to monitor and detect metabolites of pneumonia at an early stage. An electronic nose (e-nose) with 28 metal oxide semiconductor gas sensors was developed for predicting the presence of infection after patients have been intubated in the intensive care unit. The effectiveness of VAP identification was verified using clinical data. A total of 40 patients were included in this study, of whom 20 were infected with Pseudomonas aeruginosa and the remaining were uninfected. The results revealed that good accuracy rates of 0.9208% ± 0.0302% and 0.8547% ± 0.0214% were achieved by support vector machine and artificial neural network models, respectively. This study provides a simple, low-cost solution for the rapid screening of VAP at an early stage.