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Novel Approaches for Diabetes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 8483

Special Issue Editor


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Guest Editor
Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7LE, UK
Interests: diabetes; signal processing; microscopy; ion channels; microfluidics

Special Issue Information

Dear Colleagues,

The research in the field of diabetes has made tremendous progress over the last century. However, this coincided with a major worldwide change in the lifestyle of human society, which has outpaced the research, resulting in increasing rates of diabetes since mid-1980s.

There are many factors that contribute to the development of this family of diseases, the main three being insulin resistance, loss of glucose sensing by pancreatic islets, and loss of islets themselves due to autoimmunity. Our understanding of the detailed mechanisms of the pathophysiology of diabetes is not a regular progress and frequently depends on the advances in the research technology and novel approaches for studying the disease.

The Special Issue “Novel Approaches for Diabetes” welcomes original research and technological developments related to pancreatic hormone secretion, glucose homeostasis, or energy metabolism. The essential criterion is the presence of a non-medical and non-biological component, such as microfluidics, spectroscopy, analytical chemistry, data science, or mathematical modeling. We particularly welcome works from scientists whose main area of expertise lies outside of the diabetes field. The manuscripts submitted will be peer-reviewed by a panel of experts followed by a fast-tracked editorial decision.

Dr. Andrei Tarasov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diabetes mellitus
  • glucose homeostasis
  • pancreatic islets
  • energy metabolism
  • real-time assays
  • physiology and pharmacology
  • microfluidics
  • recombinant sensors
  • data science
  • mathematical modelling
  • vibrational spectroscopy

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Published Papers (1 paper)

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Research

12 pages, 339 KiB  
Article
Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study
by Luís Chaves and Gonçalo Marques
Appl. Sci. 2021, 11(5), 2218; https://doi.org/10.3390/app11052218 - 3 Mar 2021
Cited by 43 | Viewed by 8116
Abstract
Diabetes is a life-long condition that is well-known in the 21st century. Once known as a disease of the West, the rise of diabetes has been fed by a nutrition shift, rapid urbanization and increasingly sedentary lifestyles. In late 2019, a new public [...] Read more.
Diabetes is a life-long condition that is well-known in the 21st century. Once known as a disease of the West, the rise of diabetes has been fed by a nutrition shift, rapid urbanization and increasingly sedentary lifestyles. In late 2019, a new public health concern was emerging (COVID-19), with a particular hazard concerning people living with diabetes. Medical institutes have been collecting data for years. We expect to achieve predictions for pathological complications, which hopefully will prevent the loss of lives and improve the quality of life using data mining processes. This work proposes a comparative study of data mining techniques for early diagnosis of diabetes. We use a publicly accessible data set containing 520 instances, each with 17 attributes. Naive Bayes, Neural Network, AdaBoost, k-Nearest Neighbors, Random Forest and Support Vector Machine methods have been tested. The results suggest that Neural Networks should be used for diabetes prediction. The proposed model presents an AUC of 98.3% and 98.1% accuracy, an F1-Score, Precision and Sensitivity of 98.4% and a Specificity of 97.5%. Full article
(This article belongs to the Special Issue Novel Approaches for Diabetes)
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