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Bioengineering
Special Issues
Application of Advanced Mathematical Techniques to Healthcare and Medicine
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Application of Advanced Mathematical Techniques to Healthcare and Medicine

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3770

Special Issue Editor

Dr. Ehsan Nazemi

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, Faculty of Engineering and Physical Sciences, Southampton University, Southampton, UK
2. Imec-Vision Lab, Department of Physics, University of Antwerp, B-2610 Antwerp, Belgium
Interests: X-ray and neutron imaging; non-destructive testing; ionizing-radiation-based measuring instruments; electrical-capacitance-based multiphase flow meter; artificial neural network; computational techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of mathematics and engineering to healthcare and medicine is gaining momentum as the mutual benefits of this collaboration become increasingly obvious. The aim of this themed issue is to give a general view of the current research on the application of advanced mathematical methods to medicine, as well as to show how these techniques can help in important aspects such as understanding, prediction, correlation, diagnosis, treatment and data processing. This Special Issue will provide a forum to discuss exciting research on applying various kinds of advanced mathematical techniques such as neural networks, data mining, feature selection, imaging data processing, correlation analysis, etc., to mental health care, physical health care and medicine fields in a broad sense.

Dr. Ehsan Nazemi
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • neural networks
  • computational intelligence
  • data mining correlation
  • feature selection
  • diagnosing
  • treatment
  • imaging data processing
  • mental health
  • physical healthcare
  • medicine
  • prediction
  • recognition
  • imaging systems
  • numerical methods

Published Papers (2 papers)

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Research

14 pages, 2506 KiB  
Article
Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System
by In-Ae Kang, Soualihou Ngnamsie Njimbouom and Jeong-Dong Kim
Bioengineering 2023, 10(2), 245; https://doi.org/10.3390/bioengineering10020245 - 13 Feb 2023
Cited by 5 | Viewed by 1675
Abstract
The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to [...] Read more.
The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques to Healthcare and Medicine)
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18 pages, 484 KiB  
Article
Interaction of Virus in Cancer Patients: A Theoretical Dynamic Model
by Veli B. Shakhmurov, Muhammet Kurulay, Aida Sahmurova, Mustafa Can Gursesli and Antonio Lanata
Bioengineering 2023, 10(2), 224; https://doi.org/10.3390/bioengineering10020224 - 07 Feb 2023
Cited by 1 | Viewed by 1334
Abstract
This study reports on a phase-space analysis of a mathematical model of tumor growth with the interaction between virus and immune response. In this study, a mathematical determination was attempted to demonstrate the relationship between uninfected cells, infected cells, effector immune cells, and [...] Read more.
This study reports on a phase-space analysis of a mathematical model of tumor growth with the interaction between virus and immune response. In this study, a mathematical determination was attempted to demonstrate the relationship between uninfected cells, infected cells, effector immune cells, and free viruses using a dynamic model. We revealed the stability analysis of the system and the Lyapunov stability of the equilibrium points. Moreover, all endemic equilibrium point models are derived. We investigated the stability behavior and the range of attraction sets of the nonlinear systems concerning our model. Furthermore, a global stability analysis is proved either in the construction of a Lyapunov function showing the validity of the concerned disease-free equilibria or in endemic equilibria discussed by the model. Finally, a simulated solution is achieved and the relationship between cancer cells and other cells is drawn. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques to Healthcare and Medicine)
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