Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction

Haripriya S; Narmatha S; Mrs. Amsavalli K; Mrs. Maheswari M; Dr. S. Roselin Mary

Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction

June 2022

Vol-8, Issue-3

Paper ID: 17490

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science and Engineering

Keywords

Diabetes Finger Pricking Naive Bayes Logistic Regression (LR) Prediction Non-invasive Machine Learning (ML) Features

Abstract

Diabetes is one of the major problems in today's world, and it is a major health issue for people of all ages. Regular glucose measurement is a prerequisite for monitoring blood glucose levels and establishing treatment strategies for diabetes. The most common method of measuring glucose levels is an invasive procedure that requires finger-stroking and can be painful and obedient, especially if this happens in daily routine. Machine learning is a sub- field of artificial intelligence, widely described as the ability of a machine to mimic intelligent human behavior. One such method in Machine Learning is data mining. Non-invasive(devices that do not penetrate the patient's body) methods for measuring sugar and presenting classification measurements according to different criteria: size, analyzed media, method used. , opening type, response delay, measurement duration, and access to results using a web application. We set the focus on using the learning machine as a new research and development trend.

License

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Author Information

#	Name	Institute / Affiliation
1	Haripriya S	Anand Institute of Higher Technology, Kazhipattur.
2	Narmatha S	Anand Institute of Higher Technology, Kazhipattur.
3	Mrs. Amsavalli K	Anand Institute of Higher Technology, Kazhipattur.
4	Mrs. Maheswari M	Anand Institute of Higher Technology, Kazhipattur.
5	Dr. S. Roselin Mary	Anand Institute of Higher Technology, Kazhipattur.

How to Cite

Use the following formats to cite this article in your research.

APA Style

S, Haripriya, S, Narmatha, K, Mrs. Amsavalli, M, Mrs. Maheswari, & Mary, Dr. S. Roselin (2022). Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction. International Journal of Advance Research and Innovative Ideas In Education, 8(3), 4828-4832.

MLA Style

S, Haripriya, et al. "Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction." International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, 2022, pp. 4828-4832.

IEEE Style

Haripriya S, Narmatha S, Mrs. Amsavalli K, Mrs. Maheswari M, and Dr. S. Roselin Mary, "Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction," International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, pp. 4828-4832, 2022.

Vancouver Style

S Haripriya, S Narmatha, K Mrs. Amsavalli, M Mrs. Maheswari, Mary Dr. S. Roselin. Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction. International Journal of Advance Research and Innovative Ideas In Education. 2022;8(3):4828-4832.

Harvard Style

S, Haripriya, S, Narmatha, K, Mrs. Amsavalli, M, Mrs. Maheswari, & Mary, Dr. S. Roselin (2022) 'Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction', International Journal of Advance Research and Innovative Ideas In Education, 8(3), pp. 4828-4832.

Chicago Style

S, Haripriya, et al. "Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 4828-4832.

Turabian Style

S, Haripriya, et al. "Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 4828-4832.