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Diagnostics
Special Issues
Advances in Modern Diabetes Diagnosis and Treatment Technology
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Advances in Modern Diabetes Diagnosis and Treatment Technology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 455

Special Issue Editors

Dr. Tomislav Bulum

E-Mail Website
Guest Editor
School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
Interests: diabetes type 1; diabetes type 2; metabolic syndrome; diabetic nephropathy; diabetic retinopathy
Special Issues, Collections and Topics in MDPI journals
Dr. Martina Tomić

E-Mail Website
Guest Editor
Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, 10000 Zagreb, Croatia
Interests: diabetic retinopathy; prevention; retinopathy screening
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute a paper to our Special Issue related to diabetes diagnosis and treatment. Diabetes is one of the fastest-growing global health emergencies of the 21st century. In the last 15 years, the number of people diagnosed with type 2 diabetes, a condition closely related to obesity and metabolic syndrome, has increased by 95%. The most devastating effects of diabetes are its chronic complications since diabetes is still the leading cause of preventable blindness in the adult working population, nontraumatic amputations, and renal failure. Despite the growing awareness of diabetes, its complications continue to represent a significant public health problem with a high health expenditure.

Considering this context, we welcome submissions to this Special Issue focusing on the advances in the diagnosis of chronic complications and the treatment technology of modern diabetes and insights into this disease. The detailed knowledge of this harmful disease is needed to prevent chronic complications and cardiovascular disease/death and optimize quality of life.

Dr. Tomislav Bulum
Dr. Martina Tomić
Guest Editors

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. Diagnostics 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 2600 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
  • complications
  • diagnosis
  • diabetic retinopathy
  • diabetic neuropathy
  • diabetic nephropathy
  • technology

Published Papers (1 paper)

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Research

15 pages, 1113 KiB  
Article
Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up
by Chi-Hao Liu, Chun-Feng Chang, I-Chien Chen, Fan-Min Lin, Shiow-Jyu Tzou, Chung-Bao Hsieh, Ta-Wei Chu and Dee Pei
Diagnostics 2024, 14(10), 979; https://doi.org/10.3390/diagnostics14100979 - 8 May 2024
Viewed by 286
Abstract
The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after [...] Read more.
The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years. Full article
(This article belongs to the Special Issue Advances in Modern Diabetes Diagnosis and Treatment Technology)
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