Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
The Fuzzy Logic Approaches to Medical Diagnosis
share announcement

The Fuzzy Logic Approaches to Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5228

Special Issue Editor

Prof. Dr. Rahib H. Abiyev

E-Mail Website
Guest Editor
Computer Engineering Department, Near East University, Mersin 10, 99138 Nicosia, Turkey
Interests: soft computing; control systems; digital signal processing

Special Issue Information

Dear Colleagues,

Nowadays fuzzy logic is widely used in solving more difficult problems in engineering, medicine, and economics. Medical diagnosis is one of the important and complex areas where fuzzy logic systems are extensively used. Medical diagnosis is made to identify diseases as the most likely cause of a person's symptoms and also to determine the nature of these diseases. The symptoms of some diseases are often uncertain and vague. The incomplete patient data and vague nature of diseases cause uncertainty.  In these conditions, the accurate diagnosis of diseases and the solutions to some diagnostic problems become complicated. Fuzzy logic is one of the viable approaches for the development of knowledge-based systems and to deal with uncertainty in medical diagnosis. Therefore, it is needed to consider the latest trends in the development of fuzzy logic systems. The fuzzy logic-based systems can handle uncertainties and complexity of the problem and improve the performance of the diagnostic systems.

The goal of this Special Issue is to review the research articles describing the theoretical and practical achievements in medical diagnosis.

Potential topics include but are not limited to:

  • Medical Applications of Fuzzy Systems
  • Type-2 Fuzzy Systems for medical diagnosis
  • Z-number Based Fuzzy Systems
  • Intuitionistic fuzzy logic for medical diagnosis
  • Medical Fuzzy Expert Systems
  • Evolutionary-fuzzy systems
  • Neuro-fuzzy systems
  • Hybrid AI systems for medical diagnosis
  • Fuzzy-genetic systems
  • Fuzzy Decision Making
  • Computer-assisted diagnosis
  • Clinical decision support system
  • Data Mining and Fuzzy Logic
  • Medical Imaging and Fuzzy Logic
  • Medical informatics
  • Health information technology
  • Biomedical image analysis
  • Tumor segmentation
  • Radiomics

Prof. Dr. Rahib H. Abiyev
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. 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 1756 KiB  
Article
Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks
by Rahib Abiyev, John Bush Idoko, Hamit Altıparmak and Murat Tüzünkan
Diagnostics 2023, 13(10), 1690; https://doi.org/10.3390/diagnostics13101690 - 10 May 2023
Cited by 20 | Viewed by 1709
Abstract
Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact [...] Read more.
Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uterine contractions, is employed for monitoring fetal status. Using measured statistical data, the design of the system is implemented. Comparisons of various models are presented to prove the effectiveness of the proposed system. The system can be utilized in clinical information systems to obtain valuable information about fetal health status. Full article
(This article belongs to the Special Issue The Fuzzy Logic Approaches to Medical Diagnosis)
►▼ Show Figures

Figure 1

22 pages, 2294 KiB  
Article
A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm
by Thandra Jithendra and Shaik Sharief Basha
Diagnostics 2023, 13(9), 1641; https://doi.org/10.3390/diagnostics13091641 - 6 May 2023
Cited by 12 | Viewed by 2061
Abstract
This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is [...] Read more.
This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data. Full article
(This article belongs to the Special Issue The Fuzzy Logic Approaches to Medical Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop