Using artificial neural network to predict a variety of pathogenic microorganisms

Yu Hsuan Liao; Yu Ning Yu; Maysam F. Abbod; Chung Hung Shih; Jiann Shing Shieh

doi:10.18494/SAM.2020.2813

Using artificial neural network to predict a variety of pathogenic microorganisms

Yu Hsuan Liao, Yu Ning Yu, Maysam F. Abbod, Chung Hung Shih, Jiann Shing Shieh

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients’ data are used to construct the ANN model. Then, another two patients’ data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future.

Original language	English
Pages (from-to)	2375-2385
Number of pages	11
Journal	Sensors and Materials
Volume	32
Issue number	7
DOIs	https://doi.org/10.18494/SAM.2020.2813
Publication status	Published - Jul 20 2020

Keywords

Artificial neural network
Electronic nose
Pneumonia

ASJC Scopus subject areas

Materials Science(all)
Instrumentation

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10.18494/SAM.2020.2813

Cite this

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title = "Using artificial neural network to predict a variety of pathogenic microorganisms",

abstract = "In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients{\textquoteright} data are used to construct the ANN model. Then, another two patients{\textquoteright} data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future.",

keywords = "Artificial neural network, Electronic nose, Pneumonia",

author = "Liao, {Yu Hsuan} and Yu, {Yu Ning} and Abbod, {Maysam F.} and Shih, {Chung Hung} and Shieh, {Jiann Shing}",

note = "Funding Information: This research was financially supported by the Taiwan Carbon Nanotube Technology Corporation. It was also sponsored by the Ministry of Science and Technology of Taiwan (grant number: MOST 107-2221-E-155-009-MY2). Publisher Copyright: {\textcopyright} MYU K.K.",

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AU - Yu, Yu Ning

AU - Abbod, Maysam F.

AU - Shih, Chung Hung

AU - Shieh, Jiann Shing

N1 - Funding Information: This research was financially supported by the Taiwan Carbon Nanotube Technology Corporation. It was also sponsored by the Ministry of Science and Technology of Taiwan (grant number: MOST 107-2221-E-155-009-MY2). Publisher Copyright: © MYU K.K.

PY - 2020/7/20

Y1 - 2020/7/20

N2 - In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients’ data are used to construct the ANN model. Then, another two patients’ data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future.

AB - In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients’ data are used to construct the ANN model. Then, another two patients’ data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future.

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KW - Pneumonia

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Using artificial neural network to predict a variety of pathogenic microorganisms

Abstract

Keywords

ASJC Scopus subject areas

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