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

Malik, Shahid; Khadgawat, Rajesh; Anand, Sneh; Gupta, Shalini

doi:10.1186/s40064-016-2339-6

Cited by 51 publications

(27 citation statements)

References 34 publications

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“…The second category deals with disease prediction and diagnosis [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76]. Numerous algorithms and different approaches have been applied, such as traditional machine learning algorithms, ensemble learning approaches and association rule learning in order to achieve the best classification accuracy.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

954

498

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct 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. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

954

498

View full text Add to dashboard Cite

show abstract

“…Our results were comparable to similar articles, in which neural networks were used for outcome prediction of diabetes presence. For example, an SVM algorithm using the RBF kernel, the same as was used in this paper, was able to predict the presence of elevated blood glucose level via electrochemical measurement of saliva with approximately 85% accuracy [52].…”

Section: Discussionmentioning

confidence: 99%

A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device

Czmil

2019

Applied Sciences

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Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on physical activity measurement. We solved a binary classification problem using a variety of computational intelligence methods, including non-linear classification algorithms, which were applied and comparatively assessed. Prediction of disease presence among children and adolescents was evaluated using performance measures, such as accuracy, sensitivity, specificity, precision, the goodness index, and AUC. The most satisfying results were obtained when using the random forest method. The primary parameters in disease detection were weekly step count and the weekly number of vigorous activity minutes. The dependance between the weekly number of steps and the type 1 diabetes presence was established after an insightful analysis of data using classification and clustering algorithms. The findings have shown promising results that type 1 diabetes can be diagnosed using physical activity measurement. This is essential regarding the non-invasiveness and flexibility of the detection method, which can be tested at any time anywhere. The proposed technique can be implemented on a mobile device.

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“…Malik et al [94] detected fasting blood glucose levels (FBGLs) in a mixed population of 175 healthy and diseased individuals in India. Their detecting algorithm uses machine learning techniques such as logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN).…”

Section: And Nn Methods For Noninvasive Glucose Measurementmentioning

confidence: 99%

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

et al. 2020

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Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

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

Cited by 51 publications

References 34 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

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