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Book ChapterDOI

A Method for Detection and Classification of Diabetes Noninvasively

S. Lekha, +1 more
- pp 259-266
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TLDR
This paper aims at analyzing the breath as a noninvasive technique to predict diabetes.
Abstract
Diabetes a common ailment affecting the vast population of people requires continues monitoring of blood glucose levels so as to control this disorder. Presently, the common technique used to monitor these levels is through an invasive process of drawing blood. Although this technique achieves high accuracy, it encompasses all disadvantages associated with an invasive method. This inconvenience is felt more accurately in patients who frequently examine these levels through the day. Hence, there is a need for a noninvasive technique for predicting the glucose levels. This paper aims at analyzing the breath as a noninvasive technique to predict diabetes.

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References
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LIBSVM: A library for support vector machines

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI

A Tutorial on Support Vector Machines for Pattern Recognition

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI

Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

TL;DR: A WHO Consultation has taken place in parallel with a report by an American Diabetes Association Expert Committee to re‐examine diagnostic criteria and classification of diabetes mellitus and is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.
Journal ArticleDOI

Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization.

TL;DR: The results show that GC-MS and SPME with on-fiber derivatization is a simple, rapid and sensitive and solvent-free method for determination of low concentration acetone in breath and analysis of breath acetone can be used as supplementary tool for diagnosis of diabetes.
Journal ArticleDOI

A Study on Breath Acetone in Diabetic Patients Using a Cavity Ringdown Breath Analyzer: Exploring Correlations of Breath Acetone With Blood Glucose and Glycohemoglobin A1C

TL;DR: Relations between breath acetone and BG, A1C, and several other bio indices, such as the type of diabetes, onset-time, gender, age, and weight were investigated.
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