Design and Development of Electronic Nose (E-Nose) to Detect Gastric Diseases (Gastritis and Dyspepsia) Through the Respiratory Tract

DOI

10.2991/assehr.k.210909.056How to use a DOI?

Keywords

E-nose, Artificial Neural Network (ANN), gas sensors, gastritis and dyspepsia

Abstract

In this study has been designed an E-Nose system that serves as a respiratory aroma or smell detector in gastric patients. The gastric is a digestive organ found in humans that are susceptible to diseases. Gastric disease detection is carried out using Artificial Neural Network (ANN) method. Artificial Neural Network (ANN) is a part of artificial intelligence related to forecasting or prediction that can be described as a simulation of a collection of biological neural models. The design of the E-Nose system uses MQ gas sensors including MQ2, MQ4, MQ5, MQ7, MQ9 and MQ135, which serves to recognize the respiratory air samples of gastric patients. The use of several gas sensors aims to obtain the output of sensors in the form of voltage patterns, where each sensor can recognize the aroma of each respiratory sample of gastric patients namely gastritis and dyspepsia. From the ANN test results showed the accuracy of the output with the target indicated by a correlation coefficient value (R) of 0.94913. The correlation coefficient value (R) value that almost reaches a value of 1 indicates that the ANN processing is running well, with an accuracy value of 99.5%.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Muhammad Yakob
AU  - Dona Mustika
AU  - Miranda
AU  - Nirmala Sari
AU  - Rachmad Almi Putra
PY  - 2021
DA  - 2021/09/11
TI  - Design and Development of Electronic Nose (E-Nose) to Detect Gastric Diseases (Gastritis and Dyspepsia) Through the Respiratory Tract
BT  - Proceedings of the 2nd International Conference on Science, Technology, and Modern Society (ICSTMS 2020)
PB  - Atlantis Press
SP  - 254
EP  - 257
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210909.056
DO  - 10.2991/assehr.k.210909.056
ID  - Yakob2021
ER  -