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Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva

Machine learning techniques such as logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) were used to detect fasting blood glucose levels (FBGL) in a mixed population of healthy and diseased individuals in an Indian population. The occurrence of elevated FBGL wa...

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Autores principales: Malik, Sarul, Khadgawat, Rajesh, Anand, Sneh, Gupta, Shalini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899397/
https://www.ncbi.nlm.nih.gov/pubmed/27350930
http://dx.doi.org/10.1186/s40064-016-2339-6
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author Malik, Sarul
Khadgawat, Rajesh
Anand, Sneh
Gupta, Shalini
author_facet Malik, Sarul
Khadgawat, Rajesh
Anand, Sneh
Gupta, Shalini
author_sort Malik, Sarul
collection PubMed
description Machine learning techniques such as logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) were used to detect fasting blood glucose levels (FBGL) in a mixed population of healthy and diseased individuals in an Indian population. The occurrence of elevated FBGL was estimated in a non-invasive manner from the status of an individual’s salivary electrochemical parameters such as pH, redox potential, conductivity and concentration of sodium, potassium and calcium ions. The samples were obtained from 175 randomly selected volunteers comprising half healthy and half diabetic patients. The models were trained using 70 % of the total data, and tested upon the remaining set. For each algorithm, data points were cross-validated by randomly shuffling them three times prior to implementing the model. The performance of the machine learning technique was reported in terms of four statistically significant parameters—accuracy, precision, sensitivity and F1 score. SVM using RBF kernel showed the best performance for classifying high FBGLs with approximately 85 % accuracy, 84 % precision, 85 % sensitivity and 85 % F(1) score. This study has been approved by the ethical committee of All India Institute of Medical Sciences, New Delhi, India with the reference number: IEC/NP-278/01-08-2014, RP-29/2014. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-016-2339-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-48993972016-06-27 Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva Malik, Sarul Khadgawat, Rajesh Anand, Sneh Gupta, Shalini Springerplus Research Machine learning techniques such as logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) were used to detect fasting blood glucose levels (FBGL) in a mixed population of healthy and diseased individuals in an Indian population. The occurrence of elevated FBGL was estimated in a non-invasive manner from the status of an individual’s salivary electrochemical parameters such as pH, redox potential, conductivity and concentration of sodium, potassium and calcium ions. The samples were obtained from 175 randomly selected volunteers comprising half healthy and half diabetic patients. The models were trained using 70 % of the total data, and tested upon the remaining set. For each algorithm, data points were cross-validated by randomly shuffling them three times prior to implementing the model. The performance of the machine learning technique was reported in terms of four statistically significant parameters—accuracy, precision, sensitivity and F1 score. SVM using RBF kernel showed the best performance for classifying high FBGLs with approximately 85 % accuracy, 84 % precision, 85 % sensitivity and 85 % F(1) score. This study has been approved by the ethical committee of All India Institute of Medical Sciences, New Delhi, India with the reference number: IEC/NP-278/01-08-2014, RP-29/2014. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40064-016-2339-6) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-05-23 /pmc/articles/PMC4899397/ /pubmed/27350930 http://dx.doi.org/10.1186/s40064-016-2339-6 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Malik, Sarul
Khadgawat, Rajesh
Anand, Sneh
Gupta, Shalini
Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title_full Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title_fullStr Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title_full_unstemmed Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title_short Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
title_sort non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899397/
https://www.ncbi.nlm.nih.gov/pubmed/27350930
http://dx.doi.org/10.1186/s40064-016-2339-6
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