Special Issue "Machine Learning for Biomedical Data Processing"

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: 15 June 2023 | Viewed by 6581

Special Issue Editors

Department of Electrical and Computer Engineering, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Interests: event-driven systems; signal processing; circuits and systems; machine learning; computational complexity; embedded systems; battery management systems; bioinformatics; healthcare; biomedical; positron emission tomography (PET)
Special Issues, Collections and Topics in MDPI journals
Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Interests: signal and image processing; EEG signal analysis; neurofeedback
Information Systems Department, College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
Interests: machine learning; pattern recognition; data mining; biomedical signal processing

Special Issue Information

Dear Colleagues,

Biomedical signal processing involves the treatment and analysis of bio-signal measurements. It is done in order to provide useful information which clinicians can use to make decisions. Novel signal processing methods have assisted in revealing information that entirely altered the previous approaches taken in the diagnosis of different diseases. In order to analyze biomedical signals, biomedical engineers use different types of signal processing and machine learning techniques. By using intelligent biomedical analysis tools, the signals can be analyzed by software to help physicians gain greater insights and to make better decisions in clinical assessments.

Nowadays, there is a great deal of interest in machine learning applications in health, biomedicine, and biomedical engineering. The recent advances in biomedical signal processing and machine learning have brought forth incredible progress to different areas in signal analysis and processing, including biometrics, medical data processing, etc. The application-oriented and data-driven bio-signal analysis and processing applications, not only benefit from the machine learning algorithms, but also encourage the development of intelligent techniques.

The purpose of this Special Issue is to present recent advances in signal processing and machine learning for biomedical signal analysis. We are targeting original research works in this field, covering new theories, algorithms, implementations, and applications for signal and data analytics. Potential topics of interests are related to recent advances in machine learning in signal analysis and processing, but are not limited to them:

  • Biomedical Signal Processing and Analysis
  • Biomedical Image Processing and Analysis
  • Brain Computer Interface
  • Human Machine Interfaces
  • Neural Rehabilitation Engineering
  • Biomedical Data processing for Big Data
  • Information forensics and security
  • The Internet of Things and RFID
  • Machine learning for signal/image processing
  • Signal/Image Processing for Brain Machine Interface
  • Time-frequency and Non-stationary Biosignal Analysis
  • Machine learning for biomedical signal/image processing
  • Machine Learning in Biomedical Applications
  • Biometrics with biomedical signals

Prof. Dr. Abdulhamit Subasi
Prof. Dr. Humaira Nisar
Dr. Saeed M. Qaisar
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1400 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.

Published Papers (2 papers)

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Article
Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer
Mach. Learn. Knowl. Extr. 2019, 1(1), 466-482; https://doi.org/10.3390/make1010028 - 14 Feb 2019
Cited by 40 | Viewed by 4381
Abstract
Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such [...] Read more.
Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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Systematic Review
Machine Learning and Prediction of Infectious Diseases: A Systematic Review
Mach. Learn. Knowl. Extr. 2023, 5(1), 175-198; https://doi.org/10.3390/make5010013 - 01 Feb 2023
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Abstract
The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and [...] Read more.
The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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