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Glucose content analysis using image processing and machine learning techniques

Technology is constantly evolving to make it easier for people to work in biomedical research and technology daily. The glucose level checking system can use a urine analyzer detector as a color reader of the urine strip. This work aims to analyse glucose levels based on digital picture identificati...

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Main Authors: Yudhana, Anton, Akbar, Son Ali, Farezi, Andrio, Kamarul Hawari, Ghazali, Nuraisyah, Fatma, Rosyady, Phisca Aditya
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39427/1/Glucose%20Content%20Analysis%20using%20Image%20Processing%20and%20Machine%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/39427/2/Glucose%20content%20analysis%20using%20image%20processing%20and%20machine%20learning%20techniques_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39427/
https://doi.org/10.1109/ICOIACT55506.2022.9972142
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spelling my.ump.umpir.394272023-11-29T04:01:35Z http://umpir.ump.edu.my/id/eprint/39427/ Glucose content analysis using image processing and machine learning techniques Yudhana, Anton Akbar, Son Ali Farezi, Andrio Kamarul Hawari, Ghazali Nuraisyah, Fatma Rosyady, Phisca Aditya T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Technology is constantly evolving to make it easier for people to work in biomedical research and technology daily. The glucose level checking system can use a urine analyzer detector as a color reader of the urine strip. This work aims to analyse glucose levels based on digital picture identification using the MATLAB application for patient glucose data processing. Injections are joint for diabetic people to control their blood sugar levels. Repeated injections might cause minor physical harm to the body that can hamper the immune system's ability to fight against pathogens. Numerous research has concentrated on non-invasive glucose-based testing, namely using urine. This study was created using image processing to examine the non-invasive glucose testing procedure. The noise is cleaned up using a Gaussian filter and histogram-based feature extraction for picture database extraction. Support vector machines classify data using a 70% training and 30% testing process. The SVM classification results had an accuracy of 85% and time processing of 0.5 seconds. In making medical decisions, it is possible to consider the effects of diabetes, pre-diabetes, and non-diabetes. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39427/1/Glucose%20Content%20Analysis%20using%20Image%20Processing%20and%20Machine%20Learning.pdf pdf en http://umpir.ump.edu.my/id/eprint/39427/2/Glucose%20content%20analysis%20using%20image%20processing%20and%20machine%20learning%20techniques_ABS.pdf Yudhana, Anton and Akbar, Son Ali and Farezi, Andrio and Kamarul Hawari, Ghazali and Nuraisyah, Fatma and Rosyady, Phisca Aditya (2022) Glucose content analysis using image processing and machine learning techniques. In: ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding, 24-25 August 2022 , Yogyakarta. pp. 513-516. (185076). ISBN 978-166545140-6 https://doi.org/10.1109/ICOIACT55506.2022.9972142
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Yudhana, Anton
Akbar, Son Ali
Farezi, Andrio
Kamarul Hawari, Ghazali
Nuraisyah, Fatma
Rosyady, Phisca Aditya
Glucose content analysis using image processing and machine learning techniques
description Technology is constantly evolving to make it easier for people to work in biomedical research and technology daily. The glucose level checking system can use a urine analyzer detector as a color reader of the urine strip. This work aims to analyse glucose levels based on digital picture identification using the MATLAB application for patient glucose data processing. Injections are joint for diabetic people to control their blood sugar levels. Repeated injections might cause minor physical harm to the body that can hamper the immune system's ability to fight against pathogens. Numerous research has concentrated on non-invasive glucose-based testing, namely using urine. This study was created using image processing to examine the non-invasive glucose testing procedure. The noise is cleaned up using a Gaussian filter and histogram-based feature extraction for picture database extraction. Support vector machines classify data using a 70% training and 30% testing process. The SVM classification results had an accuracy of 85% and time processing of 0.5 seconds. In making medical decisions, it is possible to consider the effects of diabetes, pre-diabetes, and non-diabetes.
format Conference or Workshop Item
author Yudhana, Anton
Akbar, Son Ali
Farezi, Andrio
Kamarul Hawari, Ghazali
Nuraisyah, Fatma
Rosyady, Phisca Aditya
author_facet Yudhana, Anton
Akbar, Son Ali
Farezi, Andrio
Kamarul Hawari, Ghazali
Nuraisyah, Fatma
Rosyady, Phisca Aditya
author_sort Yudhana, Anton
title Glucose content analysis using image processing and machine learning techniques
title_short Glucose content analysis using image processing and machine learning techniques
title_full Glucose content analysis using image processing and machine learning techniques
title_fullStr Glucose content analysis using image processing and machine learning techniques
title_full_unstemmed Glucose content analysis using image processing and machine learning techniques
title_sort glucose content analysis using image processing and machine learning techniques
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39427/1/Glucose%20Content%20Analysis%20using%20Image%20Processing%20and%20Machine%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/39427/2/Glucose%20content%20analysis%20using%20image%20processing%20and%20machine%20learning%20techniques_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39427/
https://doi.org/10.1109/ICOIACT55506.2022.9972142
_version_ 1822923874841395200
score 13.232414

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