TY - JOUR
T1 - Identification of Diabetes through Urine Using Gas Sensor and Convolutional Neural Network
AU - Misbah,
AU - Rivai, Muhammad
AU - Kurniawan, Fredy
AU - Muchidin, Zainul
AU - Aulia, Dava
N1 - Publisher Copyright:
© 2022, International Journal of Intelligent Engineering and Systems. All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - Currently, blood glucose disease detection devices are administered invasively by inserting a needle into a finger or a blood vessel. This can cause trauma to the patient, especially if the frequency of examinations is very frequent. Therefore, we need a more comfortable and effective device for early recognition of the signs of diabetes mellitus. Blood sugar monitoring can also be done by smelling the patient's urine. Electronic nose is a system that can be used to identify someone with diseases including diabetes. The electronic nose system used in this study consists of several semiconductor gas sensors. The pattern recognition algorithms utilize convolutional neural network (CNN), learning vector quantization (LVQ), multilayer Perceptron (MLP), Naïve Bayes, k-Nearest Neighbors (k-NN), SVM, PCA-Logistic Regression (PCA-LR), PCA -LVQ, and PCA-MLP. The experimental results show that the CNN has the highest accuracy of 100% in the identification of two classes, namely healthy, and diabetic.
AB - Currently, blood glucose disease detection devices are administered invasively by inserting a needle into a finger or a blood vessel. This can cause trauma to the patient, especially if the frequency of examinations is very frequent. Therefore, we need a more comfortable and effective device for early recognition of the signs of diabetes mellitus. Blood sugar monitoring can also be done by smelling the patient's urine. Electronic nose is a system that can be used to identify someone with diseases including diabetes. The electronic nose system used in this study consists of several semiconductor gas sensors. The pattern recognition algorithms utilize convolutional neural network (CNN), learning vector quantization (LVQ), multilayer Perceptron (MLP), Naïve Bayes, k-Nearest Neighbors (k-NN), SVM, PCA-Logistic Regression (PCA-LR), PCA -LVQ, and PCA-MLP. The experimental results show that the CNN has the highest accuracy of 100% in the identification of two classes, namely healthy, and diabetic.
KW - Convolutional neural network
KW - Diabetes
KW - Diseases
KW - Gas sensor
KW - Urine
UR - http://www.scopus.com/inward/record.url?scp=85123508334&partnerID=8YFLogxK
U2 - 10.22266/IJIES2022.0228.47
DO - 10.22266/IJIES2022.0228.47
M3 - Article
AN - SCOPUS:85123508334
SN - 2185-310X
VL - 15
SP - 520
EP - 529
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 1
ER -