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Title: :  Non-Invasive Glucose Monitoring Using Machine Learning Techniques for Diabetics Prediction
PaperId: :  17490
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-17490
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Haripriya SAnand Institute of Higher Technology, Kazhipattur.
Narmatha SAnand Institute of Higher Technology, Kazhipattur.
Mrs. Amsavalli KAnand Institute of Higher Technology, Kazhipattur.
Mrs. Maheswari MAnand Institute of Higher Technology, Kazhipattur.
Dr. S. Roselin MaryAnand Institute of Higher Technology, Kazhipattur.

Abstract

Computer Science and Engineering
Diabetes, Finger Pricking, Naive Bayes, Logistic Regression (LR), Prediction, Non-invasive, Machine Learning (ML), Features,
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.

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IJARIIE 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 Volume 8 Issue 3 2022 Page 4828-4832
MLA 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 8.3(2022) : 4828-4832.
APA Haripriya S, Narmatha S, Mrs. Amsavalli K, Mrs. Maheswari M, & Dr. S. Roselin Mary. (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.
Chicago 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 8, no. 3 (2022) : 4828-4832.
Oxford 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, 2022, p. 4828-4832. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Non_Invasive_Glucose_Monitoring_Using_Machine_Learning_Techniques_for_Diabetics_Prediction_ijariie17490.pdf (Accessed : ).
Harvard Haripriya S, Narmatha S, Mrs. Amsavalli K, Mrs. Maheswari M, and Dr. S. Roselin Mary. (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. 4828-4832IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Non_Invasive_Glucose_Monitoring_Using_Machine_Learning_Techniques_for_Diabetics_Prediction_ijariie17490.pdf (Accessed : )
IEEE 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, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/Non_Invasive_Glucose_Monitoring_Using_Machine_Learning_Techniques_for_Diabetics_Prediction_ijariie17490.pdf [Accessed : ].
Turabian 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 [Online]. volume 8 number 3 ().
Vancouver 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 [Internet]. 2022 [Cited : ]; 8(3) : 4828-4832. Available from: https://ijariie.com/AdminUploadPdf/Non_Invasive_Glucose_Monitoring_Using_Machine_Learning_Techniques_for_Diabetics_Prediction_ijariie17490.pdf
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