A Hybrid machine learning algorithm for detection of simulated expiratory markers of diabetic patients based on gas sensor array

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 paper, seven metal oxide gas sensors were used to collect acetone and ethanol gas which are used to simulate the exhaled breath of diabet...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 23; no. 3; p. 1
Main Authors: Zhu, Hongyin, Liu, Chao, Zheng, Yao, Zhao, Jing, Li, Lei
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Acetone
Adaptive algorithms
Algorithms
Datasets
Diabetes
Drift
Ethanol
Gas detectors
gas sensor array
Gas sensors
Gases
Liquids
Machine learning
Metal oxides
prediction
Prediction algorithms
Principal components analysis
Sensor arrays
Sensors
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary: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 paper, 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 characteristics from the data collected by the sensor array. The kind of gases is qualitatively identified using the AdaBoost algorithm, and the Grid Search method is used to automatically optimize the parameters of AdaBoost algorithm. Quantitative analysis of gas concentration is performed using multivariate relevance vector machines (MVRVM) and it is also trained using the gas sensor array drift dataset at different concentrations from the 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 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 root mean square errors for quantification of acetone and ethanol gases are 11.59 and 8.72, respectively.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3229030