Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO and WO Breath Sensors.

A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO- and WO-based sensors. The six sensors, including SnO- and WO-based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO- and WO-based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO- or WO-based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed.