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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 (20 March 2025) | Viewed by 7680

Special Issue Editor

Dr. Ehsan Nazemi

E-Mail Website
Guest Editor
Faculty of Engineering and Physical Sciences, Southampton University, Southampton, UK
Interests: X-ray imaging; radiation; neural network; neutron/gamma radiography; diagnosis
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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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27 pages, 2089 KB  
Article
Dementia and Heart Failure Classification Using Optimized Weighted Objective Distance and Blood Biomarker-Based Features
by Veerasak Noonpan, Supansa Chaising, Georgi Hristov and Punnarumol Temdee
Bioengineering 2025, 12(9), 980; https://doi.org/10.3390/bioengineering12090980 - 15 Sep 2025
Viewed by 187
Abstract
Dementia and heart failure are growing global health issues, exacerbated by aging populations and disparities in care access. Diagnosing these conditions often requires advanced equipment or tests with limited availability. A reliable tool distinguishing between the two conditions is essential, enabling more accurate [...] Read more.
Dementia and heart failure are growing global health issues, exacerbated by aging populations and disparities in care access. Diagnosing these conditions often requires advanced equipment or tests with limited availability. A reliable tool distinguishing between the two conditions is essential, enabling more accurate diagnoses and reducing misclassifications and inappropriate referrals. This study proposes a novel measurement, the optimized weighted objective distance (OWOD), a modified version of the weighted objective distance, for the classification of dementia and heart failure. The OWOD is designed to enhance model generalization through a data-driven approach. By enhancing objective class generalization, applying multi-feature distance normalization, and identifying the most significant features for classification—together with newly integrated blood biomarker features—the OWOD could strengthen the classification of dementia and heart failure. A combination of risk factors and proposed blood biomarkers (derived from 10,000 electronic health records at Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand), comprising 20 features, demonstrated the best OWOD classification performance. For model evaluation, the proposed OWOD-based classification method attained an accuracy of 95.45%, a precision of 96.14%, a recall of 94.70%, an F1-score of 95.42%, and an area under the receiver operating characteristic curve of 97.10%, surpassing the results obtained using other machine learning-based classification models (gradient boosting, decision tree, neural network, and support vector machine). Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques to Healthcare and Medicine)
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14 pages, 2506 KB  
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 16 | Viewed by 3187
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 KB  
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 - 7 Feb 2023
Cited by 2 | Viewed by 2198
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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Review

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15 pages, 2622 KB  
Review
Finite Element Modeling in Left Ventricular Cardiac Biomechanics: From Computational Tool to Clinical Practice
by Patrick Hoang and Julius Guccione
Bioengineering 2025, 12(9), 913; https://doi.org/10.3390/bioengineering12090913 - 25 Aug 2025
Viewed by 615
Abstract
Finite element (FE) modeling has emerged as a powerful computational approach in cardiovascular biomechanics, enabling detailed simulations of myocardial stress, strain, and hemodynamics, which are challenging to measure with conventional imaging techniques. This narrative review explores the progression of cardiac FE modeling from [...] Read more.
Finite element (FE) modeling has emerged as a powerful computational approach in cardiovascular biomechanics, enabling detailed simulations of myocardial stress, strain, and hemodynamics, which are challenging to measure with conventional imaging techniques. This narrative review explores the progression of cardiac FE modeling from research-focused applications to its increasing integration into clinical practice. Specific attention is given to the mechanical effects of myocardial infarction, the limitations of conventional LV volume-reduction surgeries, and novel therapeutic approaches like passive myocardial reinforcement via hydrogel injections. Furthermore, the review highlights the critical role of patient-specific FE simulations in optimizing LV assist device parameters and guiding targeted device placements. Cutting-edge developments in artificial intelligence-enhanced FE modeling, including surrogate models and precomputed simulation databases, are examined for their potential to facilitate real-time, personalized therapeutic decision-making. Collectively, these advancements position FE modeling as an essential tool in precision medicine for structural heart disease. Full article
(This article belongs to the Special Issue Application of Advanced Mathematical Techniques to Healthcare and Medicine)
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