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Fig. 1 | Respiratory Research

Fig. 1

From: Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Fig. 1

Flow diagram of this study. The diagram shows our standardized procedures of data collection, data preparation, model building, model evaluation, and model improvement. When the pathogens are colonized in the lung, they will release volatile organic compounds in the breath. We collected the breath from the endotracheal tube and then analyzed the sensor arrays of an electronic nose. The electric resistance changes of sensors were first normalized and autoscaled. Then, we randomly split subjects into a training set and a testing set. We used eight machine learning algorithms to estimate diagnostic accuracy. The parameters of the algorithms are selected with bootstrapping methods. The optimized models were then applied to the testing set to assess the accuracy of the breath test

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