A Hybrid Machine Learning Algorithm for Detection of Simulated Expiratory Markers of Diabetic Patients Based on Gas Sensor Array

Abstract

The method for breath detection using gas sensor array is gaining popularity. It was found that acetone can be used as breath marker in diabetic patients. In this article, seven metal oxide gas sensors were used to collect acetone and ethanol gas, which are used to simulate the exhaled breath of diabetic patients to obtain multidimensional response data. The kernel principal component analysis (KPCA) algorithm is used to extract the characteristics from the data collected by the sensor array. The kind of gases is qualitatively identified using the adaptive boosting (AdaBoost) algorithm, and the grid search (GS) method is used to automatically optimize the parameters of AdaBoost algorithm. The quantitative analysis of gas concentration is performed using multivariate relevance vector machine (MVRVM) and it is also trained using the gas sensor array drift dataset at different concentrations from the University of California (UCI) database. The experimental results show that the accuracy of the algorithm in the qualitative identification of acetone and ethanol gas reaches 99.722%, and the root-mean-square errors (RMSE) for quantification of acetone and ethanol gases are 0.027 and 0.030, respectively. The algorithm is used for qualitative identification on the gas sensor array drift dataset at different concentrations with an accuracy of 94.55%, and the RMSE for quantification of acetone and ethanol gases are 11.59 and 8.72, respectively.