Journal ArticleDOI
Detection of type-2 diabetes using characteristics of toe photoplethysmogram by applying support vector machine
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TLDR
High accuracy characteristic features of toe photoplethysmogram are used for the detection of type-2 DM using support vector machine (SVM) and it is hoped that this work will help the clinician in screening of diabetes and adopting suitable treatment plan for preventing end organ damage.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2019-01-01. It has received 34 citations till now. The article focuses on the topics: Photoplethysmogram.read more
Citations
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Journal ArticleDOI
Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone.
TL;DR: Non-invasive glucose measurement methods are reviewed and categorized based on: the intrinsic properties of glucose, blood/tissue properties and breath acetone analysis to highlight potential critical commonalities among the challenges that act as barriers to future progress.
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A digital biomarker of diabetes from smartphone-based vascular signals
Robert Avram,Jeffrey E. Olgin,Peter Kuhar,J. Weston Hughes,Gregory M. Marcus,Mark J. Pletcher,Kirstin Aschbacher,Geoffrey H. Tison +7 more
TL;DR: It is demonstrated that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes and a deep neural network applied to smartphone- based vascular imaging can detect diabetes, opening new possibilities for non-Invasive diagnosis.
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Comparative machine-learning approach: A follow-up study on type 2 diabetes predictions by cross-validation methods
TL;DR: This study conducted experiments to predict diabetes in Pima Indian females with particular ML classifiers to identify an optimized ML model, using with cross-validation methods.
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Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier.
TL;DR: In this article, a non-invasive diabetes mellitus detection system is proposed based on the wristband photoplethysmography (PPG) signal and basic physiological parameters (PhyP) to enable easy detection of Diabetes mellitus (DM).
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Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
TL;DR: This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life and highlights the potential impact of using PPG signals on an individual’s quality of life and public health.
References
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Journal ArticleDOI
Photoplethysmography and its application in clinical physiological measurement.
TL;DR: Photoplethysmography is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue and is often used non-invasively to make measurements at the skin surface.
Journal Article
Epidemiology of type 2 diabetes: Indian scenario.
TL;DR: Early identification of at-risk individuals using simple screening tools like the Indian Diabetes Risk Score (IDRS) and appropriate lifestyle intervention would greatly help in preventing or postponing the onset of diabetes and thus reducing the burden on the community and the nation as a whole.
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Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis,O. Tsave,Athanasios Salifoglou,Nicos Maglaveras,Ioannis Vlahavas,Ioanna Chouvarda +5 more
TL;DR: A systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular.
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A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine
TL;DR: The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Le least Square Support Vector Machine (LS-SVM) and a new cascade learning system based on Generalizeddiscriminant analysis and Least Square support Vector Machine is proposed.
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Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus
TL;DR: In this article, support vector machines (SVM) have been used for the diagnosis of type 2 diabetes using an additional explanation module, which turns the "black box" model of an SVM into an intelligible representation of the SVM's diagnostic decision.